{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1kYv-vpMHTUDcvO7wqoWS-7AXBkaarDd_","timestamp":1700609779142}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"yCfJIN6oZ20S"},"source":["#Lab 3 Text mining\n","- Classification\n","- Clustering\n","- Topic Modeling\n","- Summarization\n"]},{"cell_type":"code","metadata":{"id":"yVhl1TaDkKhR","colab":{"base_uri":"https://localhost:8080/"},"outputId":"54b7805b-8684-447f-aa05-00bc823cfe16","executionInfo":{"status":"ok","timestamp":1700664738957,"user_tz":-60,"elapsed":8511,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"source":["!pip install gdown\n","# !pip install pandas==1.1.0"],"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (4.6.6)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n","Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from gdown) (1.16.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.1)\n","Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.11.2)\n","Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.4)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2023.7.22)\n","Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n"]}]},{"cell_type":"markdown","metadata":{"id":"JOZ7FBqdgfMX"},"source":["#Classification"]},{"cell_type":"code","metadata":{"id":"p9dYHg5hkL_6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700664772545,"user_tz":-60,"elapsed":2401,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"20b4b065-c06f-4360-c749-21431f26da38"},"source":["!gdown 'https://drive.google.com/uc?id=1SwQEuHyO2A9hfSK2rdRH0J-YQ426U_20'"],"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading...\n","From: https://drive.google.com/uc?id=1SwQEuHyO2A9hfSK2rdRH0J-YQ426U_20\n","To: /content/spam.csv\n","\r 0% 0.00/479k [00:00\n","
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texttarget
0Go until jurong point, crazy.. Available only ...0
1Ok lar... Joking wif u oni...0
2Free entry in 2 a wkly comp to win FA Cup fina...1
3U dun say so early hor... U c already then say...0
4Nah I don't think he goes to usf, he lives aro...0
5FreeMsg Hey there darling it's been 3 week's n...1
6Even my brother is not like to speak with me. ...0
7As per your request 'Melle Melle (Oru Minnamin...0
8WINNER!! As a valued network customer you have...1
9Had your mobile 11 months or more? U R entitle...1
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random_state=0)"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"id":"swWi2plHkjjY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700665281150,"user_tz":-60,"elapsed":394,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"d27e1f42-5575-4bc5-a3fe-5f0dbcfaeff5"},"source":["spam_percent = len(spam_data[spam_data['target'] == 1]) / len(spam_data) * 100\n","\n","print(\"Spam percentage %s \" %spam_percent)"],"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Spam percentage 13.406317300789663 \n"]}]},{"cell_type":"code","metadata":{"id":"lTa6sHnjlEif","executionInfo":{"status":"ok","timestamp":1700665319659,"user_tz":-60,"elapsed":342,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"source":["X_train_transformed = vectorizer.fit_transform(X_train)\n","X_test_transformed = vectorizer.transform(X_test)"],"execution_count":6,"outputs":[]},{"cell_type":"code","source":["X_train.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SZbt7MjdjZ30","executionInfo":{"status":"ok","timestamp":1700665362342,"user_tz":-60,"elapsed":278,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"7710f3cf-b372-4901-b909-a615a65e6ea7"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(4179,)"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["X_train_transformed.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NMrnKrg2jWiU","executionInfo":{"status":"ok","timestamp":1700665328173,"user_tz":-60,"elapsed":3,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"b8aee072-7fe6-4655-e99d-8d12c702841d"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(4179, 7354)"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"HjexPevGgk76","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700665561759,"user_tz":-60,"elapsed":4417,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"cc7a8564-2a41-4117-d6ce-54fae6174c8e"},"source":["clf = DecisionTreeClassifier().fit(X_train_transformed, y_train)\n","y_pred = clf.predict(X_test_transformed)\n","\n","print(\"Accuracy: %s\" % accuracy_score(y_test,y_pred))\n","print(\"F1 score: %s\" % f1_score(y_test,y_pred))\n","print(\"Recall score: %s\" % recall_score(y_test,y_pred))\n","\n","clf = SVC().fit(X_train_transformed, y_train)\n","y_pred = clf.predict(X_test_transformed)\n","\n","print(\"\\n\")\n","print(\"SVM\")\n","print(\"Accuracy: %s\" % accuracy_score(y_test,y_pred))\n","print(\"F1 score: %s\" % f1_score(y_test,y_pred))\n","\n","clf = RandomForestClassifier().fit(X_train_transformed, y_train)\n","y_pred = clf.predict(X_test_transformed)\n","\n","\n","print(\"\\n\")\n","print(\"RF\")\n","print(\"Accuracy: %s\" % accuracy_score(y_test,y_pred))\n","print(\"F1 score: %s\" % f1_score(y_test,y_pred))\n"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.968413496051687\n","F1 score: 0.8842105263157894\n","Recall score: 0.8527918781725888\n","\n","\n","SVM\n","Accuracy: 0.9784637473079684\n","F1 score: 0.9175824175824175\n","\n","\n","RF\n","Accuracy: 0.9727207465900933\n","F1 score: 0.8932584269662921\n"]}]},{"cell_type":"code","metadata":{"id":"h-RgCHcao-Ls","executionInfo":{"status":"ok","timestamp":1700665763879,"user_tz":-60,"elapsed":227,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"source":["from sklearn.feature_extraction.text import TfidfVectorizer\n","\n","vectorizer = TfidfVectorizer(min_df=3)\n","X_train_transformed = vectorizer.fit_transform(X_train)\n","X_test_transformed = vectorizer.transform(X_test)"],"execution_count":15,"outputs":[]},{"cell_type":"code","source":["X_train_transformed"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F3AmuQ0VlG13","executionInfo":{"status":"ok","timestamp":1700665787362,"user_tz":-60,"elapsed":306,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"48df2be7-188a-4f18-ce9c-428894a7fb23"},"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<4179x2295 sparse matrix of type ''\n","\twith 48899 stored elements in Compressed Sparse Row format>"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"xK5CIa8epCcc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700665835223,"user_tz":-60,"elapsed":3934,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"475ad3d3-d65c-43d8-d883-fd69394d0fcb"},"source":["clf = DecisionTreeClassifier().fit(X_train_transformed, y_train)\n","y_pred = clf.predict(X_test_transformed)\n","\n","print(\"Accuracy: %s\" % accuracy_score(y_test,y_pred))\n","print(\"F1 score: %s\" % f1_score(y_test,y_pred))\n","\n","\n","clf = SVC().fit(X_train_transformed, y_train)\n","y_pred = clf.predict(X_test_transformed)\n","\n","print(\"\\n\")\n","print(\"SVM\")\n","print(\"Accuracy: %s\" % accuracy_score(y_test,y_pred))\n","print(\"F1 score: %s\" % f1_score(y_test,y_pred))\n","\n","clf = RandomForestClassifier().fit(X_train_transformed, y_train)\n","y_pred = clf.predict(X_test_transformed)\n","\n","\n","print(\"\\n\")\n","print(\"RF\")\n","print(\"Accuracy: %s\" % accuracy_score(y_test,y_pred))\n","print(\"F1 score: %s\" % f1_score(y_test,y_pred))"],"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.9605168700646087\n","F1 score: 0.8571428571428572\n","\n","\n","SVM\n","Accuracy: 0.9806173725771715\n","F1 score: 0.9264305177111716\n","\n","\n","RF\n","Accuracy: 0.9791816223977028\n","F1 score: 0.9205479452054796\n"]}]},{"cell_type":"code","metadata":{"id":"-Y9tuWLTWoVK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700666344113,"user_tz":-60,"elapsed":25119,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"e2f691c1-ba69-43ab-b28e-6fce738b614b"},"source":["# Cross Validation\n","from sklearn.model_selection import StratifiedKFold\n","\n","skf = StratifiedKFold(n_splits=5)\n","\n","fold = 0\n","dtc_performances = []\n","svc_performances = []\n","rfc_performances = []\n","\n","for train_index, test_index in skf.split(X, y):\n"," fold += 1\n"," print('Fold:', fold)\n"," # print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n"," X_train, X_test = X[train_index], X[test_index]\n"," y_train, y_test = y[train_index], y[test_index]\n","\n"," X_train_transformed = vectorizer.fit_transform(X_train)\n"," X_test_transformed = vectorizer.transform(X_test)\n","\n"," clf = DecisionTreeClassifier().fit(X_train_transformed, y_train)\n"," y_pred = clf.predict(X_test_transformed)\n","\n"," print(\"Accuracy: %s\" % accuracy_score(y_test,y_pred))\n"," print(\"F1 score: %s\" % f1_score(y_test,y_pred))\n"," dtc_performances.append(f1_score(y_test,y_pred))\n","\n","\n"," clf = SVC().fit(X_train_transformed, y_train)\n"," y_pred = clf.predict(X_test_transformed)\n","\n"," print(\"\\n\")\n"," print(\"SVM\")\n"," print(\"Accuracy: %s\" % accuracy_score(y_test,y_pred))\n"," print(\"F1 score: %s\" % f1_score(y_test,y_pred))\n"," svc_performances.append(f1_score(y_test,y_pred))\n","\n","\n"," clf = RandomForestClassifier().fit(X_train_transformed, y_train)\n"," y_pred = clf.predict(X_test_transformed)\n","\n","\n"," print(\"\\n\")\n"," print(\"RF\")\n"," print(\"Accuracy: %s\" % accuracy_score(y_test,y_pred))\n"," print(\"F1 score: %s\" % f1_score(y_test,y_pred))\n"," print(\"\\n\")\n"," rfc_performances.append(f1_score(y_test,y_pred))\n"],"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Fold: 1\n","Accuracy: 0.9721973094170404\n","F1 score: 0.8963210702341138\n","\n","\n","SVM\n","Accuracy: 0.9811659192825112\n","F1 score: 0.9252669039145908\n","\n","\n","RF\n","Accuracy: 0.9811659192825112\n","F1 score: 0.9252669039145908\n","\n","\n","Fold: 2\n","Accuracy: 0.9721973094170404\n","F1 score: 0.8956228956228958\n","\n","\n","SVM\n","Accuracy: 0.9802690582959641\n","F1 score: 0.9208633093525179\n","\n","\n","RF\n","Accuracy: 0.9829596412556054\n","F1 score: 0.9328621908127207\n","\n","\n","Fold: 3\n","Accuracy: 0.9614003590664273\n","F1 score: 0.8512110726643599\n","\n","\n","SVM\n","Accuracy: 0.981149012567325\n","F1 score: 0.9241877256317689\n","\n","\n","RF\n","Accuracy: 0.9775583482944344\n","F1 score: 0.9084249084249084\n","\n","\n","Fold: 4\n","Accuracy: 0.9649910233393177\n","F1 score: 0.8602150537634408\n","\n","\n","SVM\n","Accuracy: 0.981149012567325\n","F1 score: 0.9241877256317689\n","\n","\n","RF\n","Accuracy: 0.9730700179533214\n","F1 score: 0.8905109489051096\n","\n","\n","Fold: 5\n","Accuracy: 0.9578096947935368\n","F1 score: 0.8417508417508418\n","\n","\n","SVM\n","Accuracy: 0.9847396768402155\n","F1 score: 0.9403508771929825\n","\n","\n","RF\n","Accuracy: 0.9793536804308797\n","F1 score: 0.9163636363636364\n","\n","\n"]}]},{"cell_type":"code","source":["print(\"Decision tree:\", np.mean(dtc_performances), '+-', np.std(dtc_performances))\n","print(\"SVM tree:\", np.mean(svc_performances), '+-', np.std(svc_performances))\n","print(\"RF tree:\", np.mean(rfc_performances), '+-', np.std(rfc_performances))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8vIVV9ZdnNzQ","executionInfo":{"status":"ok","timestamp":1700666426208,"user_tz":-60,"elapsed":3,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"aed69730-be95-400a-f65f-a585c6792d8d"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["Decision tree: 0.8690241868071304 +- 0.022765563320490755\n","SVM tree: 0.9269713083447257 +- 0.006851600895488041\n","RF tree: 0.9146857176841932 +- 0.014620384855622384\n"]}]},{"cell_type":"markdown","source":["## Exercise"],"metadata":{"id":"RkR19fZFTvY4"}},{"cell_type":"code","source":["# download dataset\n","!wget -q --show-progress https://www.dropbox.com/s/ww5tl89fpwxt9a0/Video_Games_5.json.gz\n","!wget -q \"https://www.dropbox.com/s/yfjx9nhrky1ifws/preprocessing_functions.py\" -O preprocessing_functions.py"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5UQGnHNDTvDF","executionInfo":{"status":"ok","timestamp":1700666496038,"user_tz":-60,"elapsed":4370,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"43c76627-5c6a-4602-9c5e-a02f30bd87bf"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["Video_Games_5.json. 100%[===================>] 146.91M 88.2MB/s in 1.7s \n"]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import re\n","import nltk\n","nltk.download('stopwords')\n","from nltk.corpus import stopwords\n","\n","from preprocessing_functions import remove_punctuation, remove_stopwords, remove_urls"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TcgNxeDpT-G-","executionInfo":{"status":"ok","timestamp":1700666521727,"user_tz":-60,"elapsed":1054,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"2f1306da-7c64-4f27-e550-3d90234b3b7d"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stderr","text":["[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data] Unzipping corpora/stopwords.zip.\n","[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data] Package stopwords is already up-to-date!\n","[nltk_data] Downloading package averaged_perceptron_tagger to\n","[nltk_data] /root/nltk_data...\n","[nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip.\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"bHT9BzIgoA26"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["data = \"Video_Games_5.json.gz\"\n","df = pd.read_json(data, lines = True, compression = \"gzip\")\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":293},"id":"zbgtRmQSUAeL","executionInfo":{"status":"ok","timestamp":1700666608017,"user_tz":-60,"elapsed":13847,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"18bd1931-53c4-4dbc-a591-59b0dbe09056"},"execution_count":28,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" overall verified reviewTime reviewerID asin \\\n","0 5 True 10 17, 2015 A1HP7NVNPFMA4N 0700026657 \n","1 4 False 07 27, 2015 A1JGAP0185YJI6 0700026657 \n","2 3 True 02 23, 2015 A1YJWEXHQBWK2B 0700026657 \n","3 2 True 02 20, 2015 A2204E1TH211HT 0700026657 \n","4 5 True 12 25, 2014 A2RF5B5H74JLPE 0700026657 \n","\n"," reviewerName reviewText \\\n","0 Ambrosia075 This game is a bit hard to get the hang of, bu... \n","1 travis I played it a while but it was alright. The st... \n","2 Vincent G. Mezera ok game. \n","3 Grandma KR found the game a bit too complicated, not what... \n","4 jon great game, I love it and have played it since... \n","\n"," summary unixReviewTime vote style \\\n","0 but when you do it's great. 1445040000 NaN NaN \n","1 But in spite of that it was fun, I liked it 1437955200 NaN NaN \n","2 Three Stars 1424649600 NaN NaN \n","3 Two Stars 1424390400 NaN NaN \n","4 love this game 1419465600 NaN NaN \n","\n"," image \n","0 NaN \n","1 NaN \n","2 NaN \n","3 NaN \n","4 NaN "],"text/html":["\n","
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overallverifiedreviewTimereviewerIDasinreviewerNamereviewTextsummaryunixReviewTimevotestyleimage
05True10 17, 2015A1HP7NVNPFMA4N0700026657Ambrosia075This game is a bit hard to get the hang of, bu...but when you do it's great.1445040000NaNNaNNaN
14False07 27, 2015A1JGAP0185YJI60700026657travisI played it a while but it was alright. The st...But in spite of that it was fun, I liked it1437955200NaNNaNNaN
23True02 23, 2015A1YJWEXHQBWK2B0700026657Vincent G. Mezeraok game.Three Stars1424649600NaNNaNNaN
32True02 20, 2015A2204E1TH211HT0700026657Grandma KRfound the game a bit too complicated, not what...Two Stars1424390400NaNNaNNaN
45True12 25, 2014A2RF5B5H74JLPE0700026657jongreat game, I love it and have played it since...love this game1419465600NaNNaNNaN
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\n"]},"metadata":{},"execution_count":28}]},{"cell_type":"code","source":["# Initial size\n","print(df.shape)\n","\n","df = df[df.reviewText.notnull()].reindex()\n","print(df.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H2Yf6kMhUAW3","executionInfo":{"status":"ok","timestamp":1700666749901,"user_tz":-60,"elapsed":575,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"2266909f-dad5-4cfd-a922-fc11eb9346b3"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["(497577, 12)\n","(497419, 12)\n"]}]},{"cell_type":"code","source":["df = df[df.verified].reindex()\n","df.shape\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"h2-jW0nUURHX","executionInfo":{"status":"ok","timestamp":1700666792876,"user_tz":-60,"elapsed":581,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"d7575cfa-5ad1-4764-f51a-3756b3900ccc"},"execution_count":30,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(332504, 12)"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","source":["og_df = df.copy()\n","df = df.sample(50_000).reset_index(drop=True)"],"metadata":{"id":"T78fv5JhUT5U","executionInfo":{"status":"ok","timestamp":1700666936340,"user_tz":-60,"elapsed":205,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":32,"outputs":[]},{"cell_type":"code","source":["STOPWORDS = stopwords.words('english')"],"metadata":{"id":"Lri86AgCpzvL","executionInfo":{"status":"ok","timestamp":1700667016321,"user_tz":-60,"elapsed":3,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":33,"outputs":[]},{"cell_type":"code","source":["# preprocess dataset\n","\n","def preprocess(text):\n","\n"," text = text.lower()\n"," text = re.sub(r'^
 ', '', text) # Video-review part\n"," text = remove_urls(text)\n"," text = remove_punctuation(text)\n"," text = remove_stopwords(text, STOPWORDS)\n"," # Remove words that are digits only\n"," text = re.sub('\\b\\d+\\b', '', text)\n","\n"," return text\n","\n","STOPWORDS = stopwords.words('english')\n","STOPWORDS.remove('not')\n","STOPWORDS.remove('is')\n","STOPWORDS.remove('but')\n","# 'not' may be relevant in classifying reviews as positive negative\n","# E.g., bigram \"not good\""],"metadata":{"id":"7qpTTNFhUWD1","executionInfo":{"status":"ok","timestamp":1700667075130,"user_tz":-60,"elapsed":314,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":35,"outputs":[]},{"cell_type":"code","source":["%%time\n","df[\"preprocessed\"] = df.reviewText.apply(preprocess)\n","# Will take a few seconds"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Bw8nic6hUer4","executionInfo":{"status":"ok","timestamp":1700667089334,"user_tz":-60,"elapsed":9581,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"08c7a29a-70bf-4827-cd10-e49fffa0b57f"},"execution_count":36,"outputs":[{"output_type":"stream","name":"stdout","text":["CPU times: user 8.73 s, sys: 14 ms, total: 8.75 s\n","Wall time: 8.89 s\n"]}]},{"cell_type":"code","source":["df = df[~df.preprocessed.str.contains(r\"^\\s*$\")]"],"metadata":{"id":"IF8q5dxzUkB4","executionInfo":{"status":"ok","timestamp":1700667110645,"user_tz":-60,"elapsed":849,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":37,"outputs":[]},{"cell_type":"code","source":["df[\"positive\"] = df.overall > 3\n","\n","print(df.groupby(\"positive\").size()/(df.shape[0]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MXIQ8-IBUl1e","executionInfo":{"status":"ok","timestamp":1700667177705,"user_tz":-60,"elapsed":257,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"eda30a00-c0ab-464e-93f8-f4fb41986f54"},"execution_count":38,"outputs":[{"output_type":"stream","name":"stdout","text":["positive\n","False 0.166556\n","True 0.833444\n","dtype: float64\n"]}]},{"cell_type":"code","source":["# now do the classification part here\n","from sklearn.metrics import classification_report # good way of presenting results\n","from sklearn.naive_bayes import MultinomialNB\n","\n","clf = MultinomialNB()\n","vectorizer = TfidfVectorizer(min_df=5, ngram_range=(1,2))\n","\n","X_train, X_test, y_train, y_test = train_test_split(df.preprocessed.values,\n"," df.positive.values,\n"," test_size=.33,\n"," random_state=42\n"," )"],"metadata":{"id":"MrVOa3h2U2Bz","executionInfo":{"status":"ok","timestamp":1700668766109,"user_tz":-60,"elapsed":3,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":58,"outputs":[]},{"cell_type":"code","source":["X_train_vectorized = vectorizer.fit_transform(X_train)"],"metadata":{"id":"1cmwdQIRujLY","executionInfo":{"status":"ok","timestamp":1700668772955,"user_tz":-60,"elapsed":5316,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":59,"outputs":[]},{"cell_type":"code","source":["X_test_vectorized = vectorizer.transform(X_test)"],"metadata":{"id":"YoxM4ayJu-5s","executionInfo":{"status":"ok","timestamp":1700668774317,"user_tz":-60,"elapsed":1374,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":60,"outputs":[]},{"cell_type":"code","source":["clf.fit(X_train_vectorized, y_train)\n","y_pred = clf.predict(X_test_vectorized)"],"metadata":{"id":"4QL4mlW6vClD","executionInfo":{"status":"ok","timestamp":1700668774318,"user_tz":-60,"elapsed":5,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":61,"outputs":[]},{"cell_type":"code","source":["print(classification_report(y_pred=y_pred, y_true=y_test, digits=5))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"v4bxnVOJvTgF","executionInfo":{"status":"ok","timestamp":1700668774318,"user_tz":-60,"elapsed":4,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"5e674935-1e9e-4f6c-f424-b9a9b3d0ff88"},"execution_count":62,"outputs":[{"output_type":"stream","name":"stdout","text":[" precision recall f1-score support\n","\n"," False 0.96482 0.07043 0.13128 2726\n"," True 0.84412 0.99949 0.91526 13729\n","\n"," accuracy 0.84558 16455\n"," macro avg 0.90447 0.53496 0.52327 16455\n","weighted avg 0.86412 0.84558 0.78538 16455\n","\n"]}]},{"cell_type":"code","source":["top_n = 20\n","\n","feature_vectors = clf.feature_log_prob_[1, :].argsort()[::-1][:top_n]\n","vectorizer.get_feature_names_out()[feature_vectors]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Mh1Cyl8qvtg5","executionInfo":{"status":"ok","timestamp":1700668778771,"user_tz":-60,"elapsed":257,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"b832a837-a8c1-4d29-e67a-face32cfe600"},"execution_count":63,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['great', 'game', 'good', 'is', 'love', 'excellent', 'fun', 'but',\n"," 'works', 'great game', 'like', 'games', 'one', 'awesome', 'play',\n"," 'product', 'not', 'nice', 'perfect', 'really'], dtype=object)"]},"metadata":{},"execution_count":63}]},{"cell_type":"code","source":["feature_vectors = clf.feature_log_prob_[0, :].argsort()[::-1][:top_n]\n","vectorizer.get_feature_names_out()[feature_vectors]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EP1xBiiPwrPI","executionInfo":{"status":"ok","timestamp":1700668817748,"user_tz":-60,"elapsed":627,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"0dd3db65-d09f-4b5a-f0e4-3d078515fb68"},"execution_count":64,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['game', 'is', 'not', 'but', 'like', 'good', 'ok', 'one', 'play',\n"," 'get', 'games', 'would', 'work', 'really', 'fun', 'game is',\n"," 'much', 'even', 'time', 'bad'], dtype=object)"]},"metadata":{},"execution_count":64}]},{"cell_type":"markdown","metadata":{"id":"kN9fBzAFpTgz"},"source":["#Clustering"]},{"cell_type":"markdown","metadata":{"id":"3ANsAbvnjs8z"},"source":["### K means"]},{"cell_type":"code","metadata":{"id":"w66e62zmpU9V","executionInfo":{"status":"ok","timestamp":1700669066988,"user_tz":-60,"elapsed":1903,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"source":["from sklearn.cluster import KMeans\n","from sklearn.metrics import v_measure_score\n","from sklearn.metrics.cluster import adjusted_rand_score\n","\n","NUM_CLUSTERS = 2\n","km = KMeans(n_clusters=NUM_CLUSTERS, max_iter=100, n_init=100, random_state=42).fit(X_train_transformed)\n","y_clus= km.predict(X_train_transformed)"],"execution_count":74,"outputs":[]},{"cell_type":"code","source":["X, y = spam_data['text'], spam_data['target']\n","X_train, X_test, y_train, y_test = train_test_split(spam_data['text'],\n"," spam_data['target'],\n"," random_state=0)\n","\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","\n","vectorizer = TfidfVectorizer(min_df=3)\n","X_train_transformed = vectorizer.fit_transform(X_train)\n","X_test_transformed = vectorizer.transform(X_test)"],"metadata":{"id":"AAzRZLepxbA_","executionInfo":{"status":"ok","timestamp":1700669066988,"user_tz":-60,"elapsed":5,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":75,"outputs":[]},{"cell_type":"code","metadata":{"id":"-CwaKS-Mqlg8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700669066988,"user_tz":-60,"elapsed":4,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"90026c22-fd76-4b52-c197-73c4fb268479"},"source":["print(f'''V measure- {v_measure_score(y_clus, y_train)}''')\n","print(f'''ARI- {adjusted_rand_score(y_clus,y_train)}''')"],"execution_count":76,"outputs":[{"output_type":"stream","name":"stdout","text":["V measure- 0.011241460562523944\n","ARI- -0.04697237772342701\n"]}]},{"cell_type":"markdown","metadata":{"id":"ed7luUxZjnE3"},"source":["### Agglomerative clustering"]},{"cell_type":"code","metadata":{"id":"7T-bgAPmhQ1s","executionInfo":{"status":"ok","timestamp":1700669291749,"user_tz":-60,"elapsed":20830,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"source":["from sklearn.cluster import AgglomerativeClustering\n","\n","model = AgglomerativeClustering(n_clusters=5, metric='euclidean', linkage='ward')\n","\n","model.fit(X_train_transformed.toarray())\n","y_clus = model.labels_"],"execution_count":77,"outputs":[]},{"cell_type":"code","metadata":{"id":"UZ4VBQybjLeY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700669291749,"user_tz":-60,"elapsed":19,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"8ca8f822-1d96-47cb-898c-5243aa809960"},"source":["print(f'''V measure- {v_measure_score(y_clus, y_train)}''')\n","print(f'''ARI- {adjusted_rand_score(y_clus,y_train)}''')"],"execution_count":78,"outputs":[{"output_type":"stream","name":"stdout","text":["V measure- 0.4005127438559348\n","ARI- 0.5672129051440428\n"]}]},{"cell_type":"markdown","source":["## GOT clustering"],"metadata":{"id":"yKub7LzkU7nO"}},{"cell_type":"code","source":["!wget -q --show-progress https://www.dropbox.com/s/b8cs502zfcdwbe5/got.zip -O got.zip\n","!unzip -o got.zip"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MggVjHO2U9ha","executionInfo":{"status":"ok","timestamp":1700670856842,"user_tz":-60,"elapsed":2154,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"80c7b25e-d870-4524-c01c-7a7bdd16db13"},"execution_count":79,"outputs":[{"output_type":"stream","name":"stdout","text":["\rgot.zip 0%[ ] 0 --.-KB/s \rgot.zip 100%[===================>] 1.54M --.-KB/s in 0.05s \n","Archive: got.zip\n"," inflating: got1_nostops.csv \n"," inflating: got2_nostops.csv \n"," inflating: got3_nostops.csv \n"," inflating: got4_nostops.csv \n"]}]},{"cell_type":"code","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import plotly.express as px\n","import numpy as np"],"metadata":{"id":"wkC84_FgVAlC","executionInfo":{"status":"ok","timestamp":1700670878649,"user_tz":-60,"elapsed":880,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":80,"outputs":[]},{"cell_type":"code","source":["got1 = pd.read_csv('got1_nostops.csv', index_col=0)\n","got2 = pd.read_csv('got2_nostops.csv', index_col=0)\n","got3 = pd.read_csv('got3_nostops.csv', index_col=0)\n","got4 = pd.read_csv('got4_nostops.csv', index_col=0)\n","got = pd.concat([got1, got2, got3, got4])"],"metadata":{"id":"KgLuxYRjVGD_","executionInfo":{"status":"ok","timestamp":1700670903021,"user_tz":-60,"elapsed":259,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":81,"outputs":[]},{"cell_type":"code","source":["got.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"ugqrm0TmVKaq","executionInfo":{"status":"ok","timestamp":1700670905564,"user_tz":-60,"elapsed":5,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"356675e4-197c-4384-e440-d7a27051e51e"},"execution_count":82,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Name Chapter Text\n","0 Eddard 1 visitors poured castle gates river gold silver...\n","1 Eddard 2 summons came hour dawn world still grey alyn s...\n","2 Eddard 3 found lord ned rose quickly men lannister jory...\n","3 Eddard 4 eddard stark rode towering bronze doors red ke...\n","4 Eddard 5 lord arryn death great sadness us lord grand m..."],"text/html":["\n","
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NameChapterText
0Eddard1visitors poured castle gates river gold silver...
1Eddard2summons came hour dawn world still grey alyn s...
2Eddard3found lord ned rose quickly men lannister jory...
3Eddard4eddard stark rode towering bronze doors red ke...
4Eddard5lord arryn death great sadness us lord grand m...
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\n","
\n"]},"metadata":{},"execution_count":82}]},{"cell_type":"code","source":["got.groupby('Name').size().sort_values()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kKYILfUPVN0p","executionInfo":{"status":"ok","timestamp":1700670930613,"user_tz":-60,"elapsed":214,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"f462aa8f-1dc8-4994-ff7e-0db00ded4655"},"execution_count":83,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Name\n","The Soiled Knight 1\n","The Reaver 1\n","The Queenmaker 1\n","The Prophet 1\n","The Princess in The Tower 1\n","The Kraken's Daughter 1\n","The Iron Captain 1\n","The Drowned Man 1\n","Epilogue 1\n","The Captain Of Guards 1\n","Appendix 3\n","Prologue 4\n","Intro 4\n","Theon 6\n","Brienne 8\n","Davos 9\n","Samwell 10\n","Cersei 10\n","Eddard 15\n","Jaime 16\n","Bran 18\n","Daenerys 21\n","Sansa 24\n","Catelyn 24\n","Jon 29\n","Arya 31\n","Tyrion 35\n","dtype: int64"]},"metadata":{},"execution_count":83}]},{"cell_type":"code","source":["names = ['Arya', 'Bran', 'Brienne', 'Catelyn', 'Cersei',\n"," 'Daenerys', 'Davos', 'Eddard', 'Jaime', 'Jon',\n"," 'Samwell', 'Sansa', 'Theon', 'Tyrion']\n","\n","got = got[got.Name.isin(names)]"],"metadata":{"id":"xtG2KIoaVQp7","executionInfo":{"status":"ok","timestamp":1700670971526,"user_tz":-60,"elapsed":267,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":84,"outputs":[]},{"cell_type":"code","source":["cdict = {'Jon' : 'black',\n"," 'Samwell' : 'tab:purple',\n"," 'Eddard': 'tab:brown',\n"," 'Arya' : 'lime',\n"," 'Catelyn' : 'tab:green',\n"," 'Bran' : 'tab:blue',\n"," 'Sansa' : 'tab:pink',\n"," 'Theon' : 'tab:cyan',\n"," 'Brienne' : 'tab:gray',\n"," 'Davos' : 'tab:olive',\n"," 'Tyrion' : 'tab:red',\n"," 'Jaime' : 'goldenrod',\n"," 'Cersei' : 'gold',\n"," 'Daenerys' : 'tab:orange',}"],"metadata":{"id":"4U8JHWmDVTdV","executionInfo":{"status":"ok","timestamp":1700670987626,"user_tz":-60,"elapsed":302,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":85,"outputs":[]},{"cell_type":"code","source":["from sklearn.feature_extraction.text import TfidfVectorizer\n","from sklearn.decomposition import TruncatedSVD"],"metadata":{"id":"n4qwYD6YVU6M","executionInfo":{"status":"ok","timestamp":1700670994859,"user_tz":-60,"elapsed":242,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":86,"outputs":[]},{"cell_type":"code","source":["%%time\n","documents = list(got.Text)\n","vectorizer = TfidfVectorizer()\n","X = vectorizer.fit_transform(documents)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oDeU68PIVUbK","executionInfo":{"status":"ok","timestamp":1700671001360,"user_tz":-60,"elapsed":869,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"eeb54145-976a-4140-e32f-015e2523b5ed"},"execution_count":87,"outputs":[{"output_type":"stream","name":"stdout","text":["CPU times: user 692 ms, sys: 2.44 ms, total: 694 ms\n","Wall time: 699 ms\n"]}]},{"cell_type":"code","source":["svd = TruncatedSVD(n_components=2)\n","data = svd.fit_transform(X)\n","scatter_x = data[:, 0]\n","scatter_y = data[:, 1]\n"],"metadata":{"id":"qXOQzqN2VYid","executionInfo":{"status":"ok","timestamp":1700671031288,"user_tz":-60,"elapsed":353,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":88,"outputs":[]},{"cell_type":"code","source":["plt.figure(figsize=(8, 8))\n","sns.scatterplot(x = scatter_x, y=scatter_y, hue=got.Name, s=150)\n","plt.tight_layout()\n","plt.axis('off')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":783},"id":"4GyurdBtVdu7","executionInfo":{"status":"ok","timestamp":1700671032419,"user_tz":-60,"elapsed":1133,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"34e4944e-b871-4385-9afb-1801ce234704"},"execution_count":89,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# 3D viz\n","svd = TruncatedSVD(n_components=3)\n","data = svd.fit_transform(X)\n","scatter_x = data[:, 0]\n","scatter_y = data[:, 1]\n","scatter_z = data[:, 2]\n","px.scatter_3d(x=scatter_x, y=scatter_y, z=scatter_z, color=got.Name)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"LCTE3GTJVee8","executionInfo":{"status":"ok","timestamp":1700671089580,"user_tz":-60,"elapsed":3753,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"f03ce9bf-33fa-4030-e0f5-faefc3b25ed1"},"execution_count":90,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","
\n","
\n","\n",""]},"metadata":{}}]},{"cell_type":"code","source":["from sklearn.manifold import TSNE\n","\n","tsne = TSNE(n_components=2, init='random', learning_rate='auto')\n","data = tsne.fit_transform(X)\n","scatter_x = data[:, 0]\n","scatter_y = data[:, 1]"],"metadata":{"id":"cc1tXZHgVpmW","executionInfo":{"status":"ok","timestamp":1700671246618,"user_tz":-60,"elapsed":101369,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":91,"outputs":[]},{"cell_type":"code","source":["plt.figure(figsize=(8, 8))\n","sns.scatterplot(x = scatter_x, y=scatter_y, hue=got.Name, s=150)\n","plt.tight_layout()\n","plt.axis('off')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":783},"id":"5lholYxxVpgw","executionInfo":{"status":"ok","timestamp":1700671247445,"user_tz":-60,"elapsed":840,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"10fe0cc5-262e-49e9-dc15-ed4d11fb2e8d"},"execution_count":92,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# # 3D viz\n","tsne = TSNE(n_components=3, init='random', learning_rate='auto')\n","data = tsne.fit_transform(X)\n","scatter_x = data[:, 0]\n","scatter_y = data[:, 1]\n","scatter_z = data[:, 2]\n","px.scatter_3d(x=scatter_x, y=scatter_y, z=scatter_z, color=got.Name)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"kK28potfVtxr","executionInfo":{"status":"ok","timestamp":1700671353161,"user_tz":-60,"elapsed":90609,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"2c7f2b21-d8d7-4f92-e51c-ef08b0d32734"},"execution_count":93,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","
\n","
\n","\n",""]},"metadata":{}}]},{"cell_type":"code","source":["from sklearn.metrics import (rand_score, adjusted_mutual_info_score,\n"," homogeneity_score, completeness_score,\n"," v_measure_score, fowlkes_mallows_score)\n","\n","def score_clustering(true_labels, predicted_labels):\n"," d = {\n"," \"Rand index\": rand_score(true_labels, predicted_labels),\n"," \"Adjusted Mutual Info\": adjusted_mutual_info_score(true_labels, predicted_labels),\n"," \"Homogeneity\": homogeneity_score(true_labels, predicted_labels),\n"," \"Completeness\": completeness_score(true_labels, predicted_labels),\n"," \"V measure\": v_measure_score(true_labels, predicted_labels),\n"," \"Fowlkes Mallows\": fowlkes_mallows_score(true_labels, predicted_labels)\n"," }\n"," for k, v in d.items():\n"," print(f\"{k:21s}: {v}\")"],"metadata":{"id":"cjH_MbWDVwfW","executionInfo":{"status":"ok","timestamp":1700671375912,"user_tz":-60,"elapsed":3,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":94,"outputs":[]},{"cell_type":"code","source":["from sklearn.cluster import KMeans\n","\n","kmeans = KMeans(n_clusters=len(names), random_state=1)\n","labels_pred = kmeans.fit_predict(X)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Il2BuIi9V0DZ","executionInfo":{"status":"ok","timestamp":1700671383857,"user_tz":-60,"elapsed":1916,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"bfa2e447-018a-472c-a219-ea43e67a0844"},"execution_count":95,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n"]}]},{"cell_type":"code","source":["plt.figure(figsize=(8, 8))\n","sns.scatterplot(x = scatter_x, y=scatter_y, hue=labels_pred.astype(str), s=150)\n","plt.tight_layout()\n","plt.axis('off')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":783},"id":"PDL8fdELV2xE","executionInfo":{"status":"ok","timestamp":1700671407627,"user_tz":-60,"elapsed":1272,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"d0d406cc-59a3-4e82-aa40-911e67abddd5"},"execution_count":96,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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C0IAwGazoby8HJmZmQFXK30y+Or9oOd6hAQQXUhISHxQ3YhXS2tQ1tULhy4QZjRgZVIMbpqVikXzQ9BapqFwkx2aG9UbrS3uB/ru4Iwh2KDipuwU3JabNmgb5beT6UIIDeUlb3ocuANAWfGrmF/wFT/0ihBCKHgnJGBIKbGlphG/PFSIDodz0LZmmx2vlNXilbJaLI2LxE+Wz8e8z5tx7G+2MQN4fxXBGC5Ip8CdTBecqygt3ORVW7utHdWVW5CavpZy3wkhPkfftIQEACElXi6twcN7jg0J3M+1v6kdd23dBy1ER/6tJmCM7BTVMn3SV6SQEPrp/zTpWrCKED+QUqCjvcTr9h1thQBlpRJC/ICCd0ImmS4kjrZ24heHCt1uU9drxQO7DiMqXUVUzuh5tTH5ypQPcoXuWkW2rURH1UdOlG92oGqHE101YmA7Ib7kKgfp/d+VptkAmvdBCPEDep5HyCRTOMOzpyrgaWb68bYufNrUgeRlIWgrGr4SDeNA4jLVJyutTgYpJKQAanY5Ub9fg71rcDBV9ZETQbEMScsNSFzi+jhjFDARH1AUIzg3QIjRn4SNxGgMpYozhBC/oJF3QiZZs9WOT+pbvGq7qawaMdkqzJHDB6yxcxUYg6fmZS6FhO4EPn3GhooPnUMC9359zRIlbzlwcpMdkKCAifhMYvIFXrdNSl0FzqbmtUcICWz0yULIJHLqAluqGz0ede+3raYJQkiEpw1NnQlJ5MjZaJrSKTPHnrOhu3bsdyduvoLk8w1gnMHRI9FRoaOtRENPve5aup7SaoiHhNCQM/tWr9pGRuUjOmbujK5DTQjxH0qbIWQyMaDD7smyqYNpUqLPqUM9ZxHVqBwFs280gSuYkikzQpdoOqqhq3r0wJ0pQN61JsTOVdBWpOPocza0lwxOITKFMyQuUZG41ADF6FqtlpCxcK4iIWkFwsIz0dVZ7lHb3Dm3QQiNKs0QMoVlZGSgsrJyyOt33303nnzyyUno0Rn0yULIZJKunPfxUDiD0AHFCMTNV5G03IDgOA4ppE8CdykkwADhBKxtAroTUM0MQdGuY/vj5oArDHX7xqhxyYDZN5oQOUvBsX/ZhwTt/eydEhUfOlH9iRNzbzUjPI1PyRsaMvGE0HDR2ifwwVt3wGHvdKtNZvY1yMze6OeeETLzCKGjufEQbNYWmC0xiI0v8OtCaPv27YOun/leOXbsGC699FLcdNNNfjunuyh4J2QSMQakhAR53T7KZITFoCDrMo6cK0yufO/TGSK+ClD7miVq9zjRdEzD2XP3jKGuEe2kpQaoFt8G8b2NAj11o4+6p11kQFSOgmPP2tBRMXZqjW4Hjj5rw8I7zQhJ4jQCP4VIKSClAMAmdNVSzlUEhyTi0iuewbYP7kFvT+0oezPkzr4VBcvvh5SSJk4T4kPVlVtwcM/jsPY1DbxmCYrD4hUPIDV9rV/OGRsbO+jfjz76KLKysrB69Wq/nM8TlPMeYLTT+cktVg0nWm043mJDU59z0DYyfaicY21KPEIN3t1Hb8xMgi7kQCDKGPNpEF36vh0Hnrai4dDgwB0AHN0Slduc2PNEH1oLdZ9NFJVCordp7HSZpOUG1O52uhW4DxxbB06+ZKcKflNAf8DudPaipnIrykveQFX5e+jsKAXQX8rR/zhXERyajCuvfxUXrHkMsfGLB203GEORO+d2XHXD61i84gHXNUh/YIT4THXlFnyy9VuDAncAsPY145Ot30J15Ra/98HhcODZZ5/FXXfdFRDXN428BwhdSDAG7KjtwX+KOnGwyTpo+7wYM27ICcclaaEAAJUe+08bCme4IiMRLxRXe9SOA7gpO3XcaTcjKXnbPnbqClwruJ7YZMecm02IzlXGffMg5dh122PnKDAEwa3+ncveJdFaqCMqV6HR9wAlpUBnRxkKjz+HqvL3oOuDlxGOjp2PnPybkZZ5+elg2b/jUP256ylpFyMt41I4nb1w2LuhKAYYTeFgYK66rIQQnxJCx8E9j2P4NRckAIZDe36O5NQ1fn0q9+qrr6KjowOf+9zn/HYOT9CnTQDQhES3Q8cX36vGd3c0DAncAeBYiw0/2tWIO96pRJtNp1H4aYQDuDM/EzFmo0ftbslJQ4zF5PP+CCHRVqJ5FhhL4NTLdujelcQehHHAMMaqsAmLDWgr1mHv9O46qNvnpMA9QEkpUVr0Ct57/TaUl7w2JHAHgNbmo9j98ffx0Qdfha7bIcXw8x18rT+INxiCERySALMlGpyrYFwJiNE4Qqab5sZDQ0bcB5Po62tEc+Mhv/bjL3/5CzZs2ICkpCS/nsddFLxPMl1I2HWJu7fUoLDdPub+lV1OfPmDavQ4KICfLhhjCDOq+M2qxYgyuRfAX5GeiK8tzPFLfzhnqN3t+Yi2cAL1B7Rxl2VkjCFilgJllLfCEs3QWeltgU2gs8r7tsR/pBSoKH0L+3f9FFKOHZA31u/F9s1fhxzHSqiEkMBls7q3Boq7+3mjsrISmzdvxhe/+EW/ncNTFLxPMs6AJw40obLL/SHLxj4N/7eniVJnphGVc6SFBuHvl67AlemJMPHhL820kCB8a3E+Hl4+1y/9kFLC1inQXurdSGb9ft+MaHMFiFs4clafamLQHd4HbFIfOzWHTDxNs2L/rv/zqE1Tw36UnNo0YTnwhJCJY7bE+HQ/b/ztb39DXFwcrrzySr+dw1OU8z7JepwCmyt7PG63q74XTX1OxFpUelw7TaicI9psxPeWzcF9BXl4vbwOFV09sOsCYUYDVifHYklcFDThGjX2x+9dCqCjzPsUBFu7hKNHwBgyznEBBqSsMKDhoIbhBmB1h4Ri9P7nZwrVew80QmgoLXpl2DSZsZQUvoTcObf5oVeEkMkUG18AS1AcrH3NGD7vnSEoKA6x8QV+Ob8QAn/7299w5513QlUDJ2QOnJ7MQJqQeK2kEw4v0l+EBF4q6sSXFkSDYpDpg58OyIMNKm7MToHCGDhj0IVE/weXOsKovK/o3q8ZBQDQ7IAxZHzHYIzBHAksu98MCAYpJBx9Eh0nBeoPaLC2SoSlev8+jKct8Q/OVZQWvuRV267OcjQ1HERM3MIJLSVJCPEvzhUsXvEAPtn6LQAMgwN41/dlwYr7/Xbdb968GVVVVbjrrrv8cnxvUfA+iVTOhp2c6q5DTX1QuP8eFZHJZTgrSHdVlJmYuzRuGF/70XLV3aEJAZVzNNvseK+qAW02BzhjSAw24/LzEpF6kQU9DQLRqQpMYQz2Ls9vfpOWGSB0SaPvAURzWtHdVeV1+9aWY4iOnQeAgndCppPU9LW48OKfDanzHhQUh4IV9/utzjsArF+/3mdlkH2JgvdJ1uf0fuJc7zjaEjIcxoHwNO+DH2MogzHY+4BYSIkTbV34+6kK7Kpvwbl/4b89UoxLUxPw+fxMBEkT0tYYUPy6Z48KjCEMMfnjL2k5HlJIyIHFtPyTAjXVaF6kywxq7+zzUU+8I4QGzlU4HN1w2DrBuAKTORKKYgIg/V7OkpDpLDV9LZJT10zoCquBjIL3SWZRvf9AH09bQobDGENQDENYKkdXtec3h4mLvf9IEVLizfI6PHbwFPQRRjrsusCbFXX4qLYJv1i5CHMWhqG9VEHLcffy9JkCzL7RBCkn6jnGGf2r3wodaCvS4eiVYBwwRzJEzlIgxczOw1dVy7jaGwzBPuqJZ4TQwBhDXfXHKD71Ahrr9w5sUxQTUjMuRe7s2xAVMxtSimkXxPfftEgpIIQG5fSjt/7XCfEVzhXEJy6d7G4EBLqyJpEuJOZEm7G3wbsRoznRZggpB/KkxyKlhJCAAFDd5UCPUyDYwJEaaoDKGRhoBJC4qrAkLTegq3rs0qVnYxxIXKZ6NaKtC4GP61vwyIGTbhX963Zq+MbHh/CXtcuRe50FmtWOjrLRbzYUIzDnFhPCUvmEj7pLIWHrkKjZ5UTTEW3IvAJTGEPCEhXJyw3ghpkZxKuqGVExc9HWctyr9okpF0x4sCiEBpu1FdvevxtdneVDtuu6HRWlb6Ki9E2kpK/F+at+6qoLPw0CeCF0SKmhouQtlBS+hPa2QgASnKuIT1yBnNm3IjH5AkipUxBPiI8xGYjJPDNIq1XDda+Vw5uqdf++Mh0poQa3gnchJZr7NLxU1IG3yrrQ6TgT6IQYODZkhuGmvHAkBRsogCcAgBMv2tBy0v3KM9lXGJG41LvqR0JKXPvWDjRZPbthWJkYg5+vXAQpJVpP6ajb50RH+eAg3hjiCoyTlqowBLFJCdzbS3Wc2GSHGKMirDmCYf5nzTCFsxkXwAuhobLsHezZ8QOP20bHLsClVz7j+06NQggNdlsH3n/zM2MsInNGQtJ5WLXut1P+Ub+UEtUV72Pfrp/C6Ri5WlpIaCouWvsrhIalUQA/Q9lsNpSXlyMzMxNms3myuzPpfPV+TP3b/yku2qLiwmTPH/cuirUgLczo9qj7ayWduOmNCvzrVMegwB1wlavcVNSBW96oxLMn2z3uC5l+pJTIv8GEmDluBBkMmHWZEUnLvLvx04TAjrpmjwN3ANhZ34Lm0+2ichUsuMOC5V+3YN7tJsy5xYRFXzBjxTcsSF9lgDFk4kfchS7RVS1w/IWxA3cAsHVIHPm7DZpVQs6wRdg4V5GeeTlM5kiP2+bOuW3C67xzrmLnRw+5HbgDQEPdbpw48heICVoR1h+kFCgveR07P/rOqIE7APR0V2PzW59DV2cF1eEnxIcoeJ9kupC4f2kcYizuj8SEGTm+c16cWyusCinxclEHfr6/eczRfQng6U9b8Y/jbQE5u5pMHMYYGAfm3GTG3FtNiMwa+vfJVSChQMWSL5mRvML7UTWVc7xcWutVWwHg5dIaCHkm1cQcwRGVrSI6T0FYimti6mRNTuUKQ+Fr9mFr1Y/E3iVR9oFz4pPyAwFjWHnxL8A9KHmUmX0N0jMvm9CRXSkFOjtK0dx40OO2JV6WwwwE/T/3vp0/xfA1t4dyOnvw0Qf3APSdQojPUPA+yRTOEG5S8Pt1KUgKHvvLJ8ai4HdrUxAfZBhzhVUpJRp6NfzqYLNHffrjkVaUdjog6MN2RusfRY/MVjD/M2Ysv9eCOTebkHetCXNvM+H8+4OQs9GIoDg+7lSryu7ecbVVhrkWJjv9y5Uuo8HW7vl11Hxcg+75g4gpj3MV0bHzsWb972Ewho65f07+LVh+4fcnZbCh6MS/vWpns7agpurDKToSzVB4/FlI6VnfrX1NqCx/d4r+zIQEHgreA4DKGeKDDPjHFen4WkEMUkOHjjolBKv48sJoPHtFOtLDjGMG7oBrVPKlog54+vRdAthU2OFZIzJtDYxoh3NE5yuIm68gOleFYmKuEXofBMkO4X3ZU4cemCVTGWeo2+ddsCJ1oP6gE8KbyTBTHOcKYuIW4uqb3kHB8vsRGpY2aLuimpGVez02XLMJS857EIyN/+bRU4xxNNTt8rp9fc1OMDb18t41Zy+qyt/3qm3xqRcp750QH6ErKUConEHlDDfkRuCW/EicaLWhvscJCSAuSMX8GDN0CbeC9n66kHi7rMur/nxQ2Y2vL45FkGEmPrsnI2GM+SWdI8xoQKvNu6Vdw4wGSCknfaR9OD3141jHoUHMuEmr/ThXwbmKnPybkTfnv9DTXQunoxuKakZQcMJAOcLJ5Bgj33s0Tmd3QP69jkYIHXU1O6B7+UioreU4bLZ2mL2Y00AIGYyC9wDTH5zPiTYjP8oEAAOTUlUPP+vLOh3o9nIhJ7sucazFiuWJk1M7mcwcupBYnRSL8i7vUmfWJMdBlxJqAAZDutP7kfNzy0nORP0jtSGhyZPck6FU1Qyno9vLtpaAveEciZQCdnvnuI7hsHdS8E6ID1DaTADjjLldTWY4PY7xpRN0OwXlvRO/UzjDjdmpULz4W4+zmLAyKQYqD8yPMtXs/fWrmhlNHA9gkVH5XreNiMqD9GQWcwBgjEFR3J9IPBzXarOETC1PPvkkMjIyYDabsWLFCuzdu3fsRn4WmN94xCeCDOP79QarfFw3D4S4K8ZiwsUpcR63uyUnLWBvMKWUw1bpcVdktmvVVRJ4hNCQk3+LV20VxYysnGunXP43YwrCI7K8bq+qQTBbon3YIzLTCKGjomk/jlW9i4qm/RNScvWFF17Afffdhx/84Ac4ePAgFi5ciMsuuwxNTe6XiPWHqfXpMcl0KSGEhCvpV0Lh4xsZ97fMcCPMCoPNi0lvKgdmR9OCCmR4/SPCUgccPRJSAKqFwWBhELr0OFdbSInvLp2Dqu4+FHW4l4pwaWo8bs9L97jvEyl5hQH1+z2ftGoIBmJnK2AzNOc90HGuIiH5fASHJKO3x7Myp2mZ66EagvzUM/9hjCEmbiHCwjOHXU12LOlZG6bcDQsJHCdrtuC9Q4+j23omaA61xOGyggcwO2Wt3877y1/+Ev/93/+Nz3/+8wCAp59+Gm+99Rb++te/4qGHHvLbecdCI+9u6K+nfqrVhheLOvDM8Ta8WNSBojb7oO2BxqQwXJoxdrm14axOCUG4aepVQyD+1R+09zYKFL3uwM7H+rD311bs+60Vu37Wh0+fsaK1UIcU0qNKKZwxGDjDHy5egvMTRh+dUxjDzdmp+NGKeQGdVsIYQ1AMR0SG5x+ziUsMM7PO+xQipY4VF/7Ao4A0KDgRC5d8zY+98i8hNGTn3ehV29zZt/m4N2SmOFmzBS/t/NagwB0Auq3NeGnnt3CyZotfzutwOHDgwAGsW7du4DXOOdatW4ddu7yvNuULdBs8iv7H8W+VdeE/xR0o7Th3Blkr8iJNuCE3HFdkhkECATcSf3NuBN4o9bzizI25EdCFHLZ+NpmZpHSNsBe+ZkPT0eEfV3ZWCnRW2mGJYpj/GTOMYXB7FF7lrpJ/v7qoAGWdPXihuBofVDegT3OdK8ZsxMbMZNyYlYJoy9TInZXCtVLtoT/ZYO9y70YjIpMjfbVh0haWIu7hXEVsQgEuXPM4dn70bei6bdT9Q0JTsGb9UzCawsDY1Bw341xFVt71KC95A+1tp9xul5V7A8IjZvmxZ2S6EkLHe4cex/CLgrkyId4/9HPkJa0B574dcGxpaYGu64iPjx/0enx8PE6dcv/v3x8oeB+BkBK6AL6zox4760auglHYbsf/7WnCzro+/OiCBDAETgUBzhhmRZhwx5xI/ONEu9vtbswJx4JYix97RqaqEy/a0VY0dp6htU3i0F9sWPzfZhhC3A/g+yetZoQF48El+fj20tlw6AKcuYL7qXZDyTiDagEWf8mM2j1O2DoAZ69ER4U+7Kqr0fkKZt8wNW5MiCsPPDFlJS6/5gUUHv8nKkrfhqb1DdonKDgBWbk3IHf2LVBU86SnjgihgXMVdnsnHPZOMKbAbIk+PZlUjnljwZiKNet/j63vfQkd7cVjni8983IsPX/y0gvI1FbVcmjIiPtgEl3WRlS1HEJG3NIJ69dko+B9BAzAwztHD9zPtq26Bz/Z3YgfXpDg34554UsLYyAB/NONAP6GnHDcuyTW/50iU4rQJWp3O90K3Ps5eyVObLKj4Iue3wie/QTLqJwJJqZS4A6cfnrHAUMQR+pqIxhz/Wy2PoGmgxrq92tw9EjEzlWRvFxFaLIy5UoIznScKwgJTcaS8x7ComX3obb6I9itbWBcRUhoChKSVkBKERBBO8BQU/khik+9iObGgwPbFMWEtMz1yJl9G6Ki80f9G+RcgcEYinVX/h0nj/4NpUWvwGZtGbJfRGQOcmbfiqzc6wI6vY0Eth7b0L+t8ezniZiYGCiKgsbGxkGvNzY2IiFhcmM9Ct6HoQmJfQ192F7jWd3pDyq7cU1WGBbEWgIuyPjywhhclByMF4s68FF1L5xn5ekrDLgoOQQ35oajIH7qTaQi/sc4vFottLtWoLtOR0gCn1FpIP1PCMq7evFCcRU+rGlCj1MDB5AUbMHVs5Jx7fJkpF5ogRSu97f/qTAF7lNP/2i1qpqRmr4OUgowAIwrp1eAndw0GSE09PU2Ytv7d6Onu3rIdl23o7zkDZSXvIG0zPU476KfABi535wr4FzB3IX/g7kL/we1VdvQ3noSmmaFwRiCxOSViImbf/qGgf6mifdCzDE+3c8TRqMRS5YswZYtW3DttdcCAIQQ2LJlC+655x6fn88TFLwPQ+UMLxV1eNX2paLOgA2A86PM+OH5CehaInCsxYoep0CQyjE3xowoswo9QCfekskldIn2Uh32Tu/+Pur2asi9ZvJXxJwomhCwaQLf2XUEe5vaBm0TAGp6rfj90RL8+XgZ7sjPwBfnns4FpvhmWnDl3QbOZH8hNNisrfjgrTtht7WNuX9V+ftwOvuwau0TY+7bn2OcnLYayamrAMZOj9rz09spxCDjkxZTgFBLHLqtzRg+750hzBKHtJgCv5z/vvvuw5133omlS5di+fLleOKJJ9Db2ztQfWay0JU1jOY+DXvq+8becRgf1/ag064HZKWW/qcB4SYFFyQFQ0iAszOjIoH2tIAEBsaAthLv6+m2legzZuRNFxI2XeCLH+5FRffonyEOIfDnE2XodDjxzYK8CeohmWk4V7Fj6wNuBe796mt24NSxfyBv7mfcCsApSCf+wrmCywoewEs7v4X+Mt1nuL5X1hfc7/PJqv1uueUWNDc34+GHH0ZDQwMWLVqEd999d8gk1ok2Nae8+1lph33Y+zt36BKo7Ar8dc0ZY1A4mzFBFfEe4wy6zfunMto42k41Cmd4ePfRMQP3s20qqcZbFXXQBK3IRHxLCh1tLSfR1nLM47bFpzZNeroPIQAwO2UtbrzgZwi1DJ6PF2aJw40X/Myvdd4B4J577kFlZSXsdjv27NmDFStW+PV87qDb5XNIKb1a1OhsNo2+hMn0IYVEaKqC5uO6Vyt+KjMkY0ZIidoeK3Y2tHrc9t9FVbgyI8kPvSIzGuMoOvlvr5r29dajrmYHEpMvoJF1Mulmp6xFXtIaVLUcQo+tBSHmGKTFFPhtxD3Q0RV5DsbYuFNeAjFlhhB3aUJAYQwddidsug6LqiB5mRFR+RxNB3TUH3BVSHFXSMLMGb17objKq3YlnT042tKBOVHhlL5GfIYxhoa63V63r6/5BInJF/qwR4R4j3NlRpWDHA0F78OYF2NGmJGjy+H5MGOMRUF2JNVpJlOPkBK9Tg0vl9bg1bJa1PedWXQmLSQI12Wl4OoLkpB4vhmnXrSjo8y96yNpmQFCl27Xep+qOGP4qLbZ6/Yf1jRhdlQYaOYq8SWno8f7ts6eGTuySUggmzlDYh7gAK6cFeZV241Z4cNPiCYkgEkp8U5lPa5842M8dax0UOAOAFU9ffj1p0W46s2Psbu5BXP/y4zIrLG/1I2hDNF5yrQP3Pt1Opxet+1wOAcWqSLEVxTV7HVbVbVAepMrRwjxKwreh8EZcFNuBIwePr42KwzX59BjbzK1CCnxenktfrzvBBxjTJq06joe2nUE2+uakX+zEebIUf7WGZB79QxJeD9NHce1b+SM7vuJz0VGeV/JKCIqF1J4X2mKEOIfFLwPgzGGGIuK750fD3e/ixUG/L8LExBupEeMZOoQUqKmpw8/O1jofhsAP9x7DJ1OJ5JXGIbdhynA7BtMiJylzKjFmdJDg71umxoaBJ1WoiQ+JISGnPybvWqrqkHIzL4aXBn+GieETB4K3kegcIaLU0Pw05WJCFJHf5tCDRyPr07C+UnBNOpOppwXiqs9DhrtQuDl8hrELlLAz/pu5wYgYbGKJV+2IGb2zArcNSFw7axkr9oaOMM1mckwcPpIJr7DuYrktFWwBMV53DYj6wooM6VUFCFTzIz8ptDOWklUSDniyqKcMVyYFIw3rsvEN5fGIjN88AdZToQRDy6Lw2vXZWJpfBA45auSKcYpBN6prPeq7evltVAMDPNuMyHrciPyrzfh/PuDkHOVEZYoNqMCdwBQOceG9ESEGDyvA3BJSrxX7QgZi5QSK1b+EIy5//cVEpqCBYsnd/l3QsjIZsy3hS4kFM7QZtPwekknKrudcOoS4SYFq1OCsTwxGJqQQ3JWFe5azOjqrHBcnxMBmyZg0yTMKoNZ5cO2IWSq+LSlA32adzmtLTYHyrp6kJ0egrA010qsAwH7DL0kVM5wz/xsPHrwlNttwo0GfGleFgQASrojvsa5ivjE5bhgzaPYtf07EProiwiGhqXj4suehmoImlaLNOl6/2RyCcY41a4nU9qM+OvVhERDrxNPHW7Fx7U9OHcNpldLOpEcYsAteRG4ITcCUsohK4/2B+hmlcOsDn2dkKlASgkBABLQpEDXOKqjAECXwwnG2UyN1YfgjOHarBR0Opx46ljpmPtHGA14YlUB4iwmqjRD/IYxjuTU1dhw9Qs4deJZVJa+A00bvApwcEgysvNuRHb+TVAU47QIboXQwbkCm7UNVRXvw25tA+MqgkOSkJa5/vTPSCuNk6ln6l+dY9CERFG7HfdtrUW3c+RKGrU9TvzyQDNOtdnx3fPiJ7CHhEyc+j4bXiyuxtsVdXho6WyYlPGN9VrUyRkrHu4GO5DckZ+BnIhQPFtYiYPN7UO2mxWO9WkJuGvOLMSYjVAp1534GecKQsJSsfS8b6Ng2X2oq/4YNls7OFcRGpaGuISlkFKfFkE7AEgp0N52CieP/g21VdshpTZo+8G9P0Nm1kbMnv95mMwR0+bnJr7z1FNP4amnnkJFRQUAYO7cuXj44YexYcOGye0YpnnwLqREc5+G+7aNHrif7e3yLkSaFdy9KMbPvSNkYsjTk1F/dbgIm0qqB8oRNvbZsCY5DhyuCjKeMnI+ruoqnpBSAhKQEuiqFnD2SnADEJrEoQa7gviz55xoQkxqQMwYw/L4KFyQGIOq7j58UN2ADrsTKmNICbFgQ3oizKdvfGiuzPQghA7GOIRwwuHoBgOD0RgKxlX0p2pMtv4+qKoFKelrIaUAA8C4AsaYR3nxgUxKgfKSN7Fv50+GBO39nI4eFJ38N6oq3seaS3+PsIhMCuADnC51HGo9gRZ7O2JMkSiIngOF+W8AKSUlBY8++ihycnIgpcTf//53XHPNNTh06BDmzp3rt/O6g0k5fWuTCSnx6N4mvFXW5VE7lQOvXZOJCDNdyGR6+H97j+Ptcyam5oSH4J/rz8M3Pj6EXQ2tHh9zQ3oiHl42x+8j4FJI2Lsl6vY40XBYg2YFQhI5cq4zIDRWRWl7D14ur0F1Tx80IRFuNOCSlDhcnBIHBjbpFaCElNClBAeDhIQEqKrMNNKfmtHSdARFJ/+NmsoPIYQrHU1RTEjLXI+c/FsRFTPbFSwHQBA/nQmho656O3ZsvR/urphoMkdi/VXPwhIURyvK+pjNZkN5eTkyMzNhNnu/YNiH9bvw+LE/ocl25rsqzhyNB+b9Ny5JPN8XXXVLVFQUHn/8cXzhC1/wqr2v3o9pHZ1aNYnNld0et9ME8EpJJz47JypgctqllNClawEpGqkj7tKEwFsV9UMCdwAo7uzBkZYO3JCV4lXwfnN2qt8nWUop0XJKx6mX7ZCn59VGZHLMvs2EUx1d+PWWYhxr6xzSbmttE6IOG3FzTio+NztzUtNsOGNnXbN07U4nQujQdRu2vX8fmhr2Ddmu63aUl7yB8pI3kJSyCheseRScGyhA9CPGgP27H4EnS53bbe349MBvcP6q//Nfx4jXPqzfhQf2Pzrk9SZbKx7Y/ygeX/qQ3wN4XdexadMm9Pb24vzzJ+5mYSTTdgjAqUu8V9EF+7mzU930RmnXpAfuQkoIKdHnFPi4thfvVXTjw6oeVHW5qgVoI5S4JKSfyjleKK4acfuLJdVYmRSLdSmezfO4blYyZkeF+XWSpRQSbcU6Tr50JnAPjueYfasJOxtbcPf2A8MG7v3a7A48fawU39t9lFYuJT4nhIAQDmx55wvDBu7nqqvZjm3vfwWQAlJ6k6hGxiKEhprKbbBZWzxuW1O5BQ6HZ0/pif/pUsfjx/406j4/P/Zn6NI/KwEfPXoUISEhMJlM+PKXv4xXXnkFc+bM8cu5PDFtR94ZA+p6vK+k0dSnQUg5aaPcQkpUdjnwwqkOfFDZDds5NyELYsy4PjcCa9NCANBoPBlKFxLH2jpR1tU74j6bqxuxLC4KDy+fC11KbK1tGvO4GzOS8MDifF92dVhSAIWv2AcNoGVuMKC0uwff33MMTjdvXjdXNyLWbMLXF+X6qadkJmKMYf+uR9DRVuR2m5amT3Fo/6+wePn9fuzZzMW5ipLCTV61FUJDaeF/kD/vDsp9DyCHWk8MSpUZTqOtBYdaT2BpzHyfnz8vLw+HDx9GZ2cnXnrpJdx555346KOPJj2An9Z/oV4OugNwxQvidJrKRJNS4vXSLvxyf9OIP8ORFhuOtDTgnfIgPLIyEQaFAngymIDEjrrmMff72cFT4Izhp+fPx3tVDfhPSc2wI9qLYyNxc3Yq1qR4vlqjp4Qu0fipBs125rWgWIaodBWP7CiDQ3g2crmppBp3zs5AhIlWjCS+4XB0oar8PY/blRe/joVLvgZV9T7flYyss6NsXG0pcA8sLfah1brGs5+njEYjsrOzAQBLlizBvn378Otf/xp/+MMf/HI+d03bv1IpgUiT93mFwQY+KWkzQkq8W96Nx/eNPQIKAHvq+/DQx/X4xZokP/eMTDUKY+h2Dl9p4Wy6lPjp/hM41tqBW3PT8Oe1y1DY3o3jbZ3o0zQEqyqWxEQhLTwIfV06hJDgfr42uMJQt39w35OWGdDUY8Mn9Z4/EtekxMulNbgjP4PKMpJxE0JDyamXBiamekLT+lBe/Dqy8q6nQNEPdN0+KW2Jf8SYIn2633gJIWC3T/7fybT9FlM5cGl6qNfTw9amhUBMQiGePqfA4/vdC9z77W3ow2slnZQDTwYRUsKkuH+Jv1Zeh9ve2427tx1AeVcPciNCcEt2GtaFJ8BQZsChP1vRuFf3ZB6Y14Qu0dsweHQ9Io/j7ep6r8paAsBbFfUUuBOf4FxFfe0Or9vX1e6gwN1PTKZwr9sajWE0HyHAFETPQZw5etR94s0xKIj2fRrLt7/9bWzfvh0VFRU4evQovv3tb2Pbtm24/fbbfX4uT03bbzLGGBJDDFiWEORV+5vzInzbITdowpUu4/Ai3+fl4s5Jn2BLAosEkBMe6nG7g83t+OHe4/j5oUIYFI7S150oft2B7loBYyibmOB9mAFNo4Whoc82dIObGsfRlpBzORyeVzLr5xxHWzIyKQWSUi7yun1K+sUUvAcYhSl4YN5/j7idAbh/3hf9Uu+9qakJd9xxB/Ly8rB27Vrs27cP7733Hi699FKfn8tT0zZ4B1zB8GfnRHqct74iMQiZ4aYJzyFXOcOrJSNXzxhNWacDx1qsk/K0gAQmA3et4hlq8G6E7/pZKehp09FV7foyYxyIm6+Cq/6/LrhhmBcZw3j+vOnKIL5kscR6PXquKCYf94a4MOTOuc2rlsEhiUhMvoCeiASgSxLPx+NLHxoyAh9vjsHP/Fgm8i9/+QsqKipgt9vR1NSEzZs3B0TgDkzjnHfAFQwvirPgawWxeOLg2BP3AGBWuBE/vjARupR+LYM3nB6HjtpxVMg51mJDXqQZVEKY9FM4wxUZiXihuNqjdqEGFZelJaDmwzN55zGzFRiCJuaa4ApDaDJHd+2ZUTCHVSDG4v2E0xgzTVYl4+Na09C1WurFlz0FAGhrOYHiU5tQVf4edN29pzvhkTm0YJMfMMYQEpqCxJSVqK/xLK0pZ/Zt9DsJYJckno/VCcsndIXVQDatg3fAVYHlxtxwRJg4fn2oBe224WuBMgCrUoLxvfMSYFTYhAfuALyuSd/PqtHjPjIYB/DFObPwSV0Lanqtbre7vyAPXDA0HnYF70wBUlcaIIUEm4D0LKFLJC1TUVjrGHits0RgQ1Yi/nqi3KtR9MvSE6ELOekrrpKpRwgNnKvo7alF8alN6O6qhBAaTKYIpKRfgmUXfA+Llt2L45/+GUUnnhvzeLmzbwEt2OUfQug4f9VP8cGbd6C7q9KtNqkZ65E/9zN+7hkZL4UpfikHORVN++AdcN2NX5IWiovTQrGtugevlXSisssBh5AINypYlRKMG3MjEB9smJQR934WdXx3/MEGGjEggzHGYFEVPLlmCe756CCqe/pG3V9hDPcX5OHS1ASceMEOzepKl5l9gwnBcXxCAnfANfIeO09F2fsOOE93uX6/hsUFQVgaF4V9TW2jtg81qLgiIxHrUuMRbTZBZQyhRsOgFDqhS3CFQQoJoblSdRhjA68TAriCwc72Uhze/ys01u8dsr2y7G0EBcUjd85/YfHybyI0NBUH9gxdDbJffOJyhISm+LPLMxrnClTVgnVXPoOPt3wDLU2HR9mbITvvRiw578FJXYWZEE/NiOAdwMBo25rUEKxLHzyJTxdy4Et9sgJ3AAgycORGmlDU7l0ZouUJQfCguAiZIVTOEW024pl1y/F8cRVeLatFs3Xw35jKGFYnx+K/ctKRHxmGwlfsaCvSERTLkH2FCeFpExe4n232TWYc/acNUgA9dQIddRo+n5+Jg83t0IdJgA83GnD3/GxclpYAMGBbTRN2N7TCKSQijAasTY1HfJAZmi7QVS5Quc05KDUnLI0jebkBMbMVSAkK4mc4ITQ01e/Hx1vvg66NnBLT19eIw/t/hY62Iqy46Eew29tx7PDQOtAGQwgWr3hwYCSf+AfnKgyGEKy74q9oaTqCopP/Rk3lhwOlPY2mCMzKvho5s29BcEjiJPeWEM8xKWmGY6DQhMTbZV14zM0a72dbGGvG79el+qFXZDrRhABjDLsbWlDZ1QeHEIgwGbA6KQ6RZiPaqzQ0HtQhdYmkZQaEpymTOhIthUR7uY6TL9qhO4DwNI55d5jxZmUdHjtwclDZyMQgM369qgDBBhXPFVbizYp6dDkGzyHhAFYkROMzuRkoiI1AyZsONBwaWgvfGMIw+2YTwpIn56aFTD4hdHR3VeKDNz8LTXM/5Sx/7h1YtOxevPmfa9DTfWauidEUjlVrf42omDkUuE8gIXRXHrsUcGp94EyFarBACh1gnEbb/cxms6G8vByZmZkwm2lhMl+9HxS8BxiHLnDNq+XocniWv/7jCxOwKiWEykUSt+hCQkgJCYAxQAUbEqROVH77WISQ0G1A/X4n6g9oCEvlyL3OhB31zfjNkWLU9loRYTTgT5csgyYF7t1+CI3W0Z9eMQD/uyAH/5WXjhObbGg5MXQuDFOA+Z8xIyyV0wj8DPXJtgdRXfGBR204V3H1Te+govQtHN7/BAzGEGRmbUT+vDtgtkRPm8D9zDyAejgc3VAUE4JDEsBPl4qiiZ8EoOD9XL56P6bHp8g0whnDT1cm4hvbauHu/NOrZoXhkjTP63mTmUvhDMoYE+YCIXAHAM4ZeBCQcqEBqRcZ0Nsk0dcscEFCDFanxGFPQyuMCoNZVXDXlgNDUoKGIwH85kgxQgwqrrg2ER3lVpw7uCp14PjzNiz/ehC4xT8/GwlcNls7aqu2etxOCA2lRa8gb+7tCAqOR1LqKjDGUV+7EwZDCOISlkzpwFYIDbpmQ2nRKygpfGnQ0wXVEIyMrCuRO/s2hIWnT2IvCZnepu4nyDSlcoaFcRb8YnWyWxNQr88Jx4PL48bcb7jVV2lFVjKVcIW5SsHFc4QmKDCcnuC9JC4SC2Mi8dcTZW4F7mf7zZFi6JBIKBiusDyg24H6A06IcVaCIlOL0J0oL3kDQgxNqXJHecnrUFULwsJn4eihp/Hai5djx4ffxLb370ZF6ZtTdiEgKXU0NezHa5s24PD+Xw0K3AFAc/ai5NSLePuV63B4/68nqZeETH808h6AFOaqT//y1Rl4vbQLr5Z0Dqr/blYYLk0PxU15EciKGH2xD/10gL69pgevl3ahptsBTQARZgWXpIbg2uxwhBpdQRDl/pGpSOUcVk3He1UNHrftcWp4p6oely5NRM3O4ddYqN+vIfXC4YN7Mk0xjr6eeq+b9/a4/hZPHv0bKsvfHXhdSh17P/kxgkOSERO3cEql0AihobnhILZv/ppbNzWnjv0dum7HkhXfmoDeETKzTJ1PjhlG5QwhRgW35EXgv2ZHosXqREufDqPKkBhsgMmNHFwpJT6o7MZTn7agxTo4p7fZqqG43Y6/HmvDFZmhuG9pHJikGthk6nEKgXcq69GnDb+Gw1heKavFtVkpiMpV0FY09Bj2Tom+JongeLo2ZhIpvft7cjm9KvEwC8hIqePY4T/gksv/OI7jTzwhNOzY9oBHTyOKTz6PhKTzkZh8/pS6USEk0FHaTADqT2dx6ALdTgGrJhBjMSA70oRZ4SZYVA7OGPgoI+VSSvz7VAd+vLtxSOB+NqeQeK20C/durYUAIGj+MpliGIDyrl6v21ecbjvnZhMSCoYPMJx9dF3MJFIKmC0xXrc3maMAAA5H17Dbmxr2o7urasqkzwihobzkTTgd3R63LTrxLwrcyZS0fft2bNy4EUlJSWCM4dVXXx20XUqJhx9+GImJibBYLFi3bh2Ki4snpG8UvAcQTUhoQmJbdQ/u3lyNi18sxZUvl2HdplLc9Ho5nj/Vjm6HPmaArQmJXXW9+P3hFrfPfajJip/tbRz1hoCQQMQZg0P3fpTUIVwBlDjVityrTUg+f2igwSlrZkbhXEVG1pXwdhXU9MzLoGk2tDR9OuI+ZcWvTZngnXMVJYWbvGrbWL8XPd21oMJ2ZLx0KXCguQrv1ZzEgeYq6H6+fnp7e7Fw4UI8+eSTw27/2c9+ht/85jd4+umnsWfPHgQHB+Oyyy6DzTbymhC+QrfDAUIXEmUddnxrez2arUMfS9b1anjq01b85WgbvrY4BtflRIx4LJUzPHO83eMl5N+r6MaXFsYgxkJ/FmTq0KVEmNH76Dr0dFt9ezVkWQeyNmbD1i7Resp1Q8A4YImicY6ZhDGGkNBkxCcuG3ZV1TFaIzv/JlSVvTviyDsAWPuahk2rCUQOexc6270dUZRoqNuFWdnXgCl0F0y882FdEX5xZAuabD0Dr8WZQ/DNBWtxSVKuX865YcMGbNiwYdhtUko88cQT+N73vodrrrkGAPCPf/wD8fHxePXVV3Hrrbf6pU/96BspAGhCorjDjru31AwbuJ/NISR+vr8Z/zrZPuwIvJCum4DjrZ7f+ekS+E9Rx8AkV0KmAs4YViePXXFpJBclxkAKCVnTDe2dcmiHGjHrkjNBRlSuAkMQPZGaaYTQMGfBFz0u65iacSlCw9JQPNZItZw6RQKczr7xtXf0QHo8nESIy4d1RXhw72uDAncAaLL14MG9r+HDuqIJ71N5eTkaGhqwbt26gdfCw8OxYsUK7Nq1y+/np+A9AEgJPPBRHaya+x9uvz/cgpOttiGBtpDAuxWe5yX221zZTZNWyZSiMIZ50eHIDg/xqv3Ns1KgHWmGbHeVmdQ3V8ISqyA8w/XxmLzcAEE3tDMO5yriEhZj8YqH4G76TEzcQqxY+QNUlL6F9taTo+5rCY71uhTlRFPU0auajd3eAm9TkMjMpkuBXxzZMuo+vzz6od9TaM7V0OCqKBUfHz/o9fj4+IFt/kTB+yTThMT7lV1os3mWsysBPH+qY0igrTCgzeb9F0Krh/0gJBBoQuC23DSP2y2IDkdeTDjE9jP1qkV5J/TqbiQtVREzW0FEpgJON7QzEmMc2XnX48I1jyEoKH7E/ThXMSvnGqxZ/xSaGw9j7yc/GvPYmdnXTJnFmkym8FF//rHExM4H51MjRYgElsMtNUNG3M/VaO3G4ZaaCepRYKDk5kmmcoaXizu9aru9tgcdNg0R5sG/xvFMOqVV4MlUpHKOKzOScKy1E6+U1brVJt5iwqMr5sNZ2g5R2DZomzjUiMi1mYjOVyGl9Gl6g9QFmDI4aJNCAmzqpFHMJIxxJKetQUr6Jait3o7Swv+gu6sKQjhhMkUgJX0tsnKvhckcibLiV3Fg96NjjqjHxi+eUiuQSimQlXc9jh56yuO2kVH5iIqZ44dekZmgxe5eJTF39/OVhIQEAEBjYyMSExMHXm9sbMSiRYv8fn4K3idZl13HqTbPVoXspwlgZ10f1meEQj09MqhLIDHY+19rfDBNKJpKpJBgp3/3QpNgiisAFLoEn4F3Yg8umY1QowHPFVZCH6W6RX5kKH5x/kKE9urQnv4U56bjyl4nVAv3aUAthQSEhL63HtqOWsjGXtdFHGyAsiAW6sVpYPHBg36nM4EQ2pBSgsO9Npn6+5KUshIpaWsGbXM6+1BS+BJKC19CT/fYo3+MKZi36EsB9zOOhnMV2Xk34finf/I41Sc7/6Yp9bOSwBJjCvbpfr6SmZmJhIQEbNmyZSBY7+rqwp49e/CVr3zF7+enq2mSdTvGl6bS7dBxdoyiMOCqWeH467E2eJOme9WsMGhCDtwMkMDUH5x3VgnU7XWirVhH/3dqUCxD4hIDEgpUcMPMG829e342bslJxX9KavBaeS1abQ4Artz4VUmxuDkrBQXxUXCWd0B76jDQO8zKqgoDpATjvktr0HfXwflyEdB3TvDTYYe+vQb69hrwOdEw3jUf0qQMGZ2fbqQU0DQbyopeQUXZ27D2NYMxhqDgBGRmb0RG1lVQFGNApZYMF4CqqhmtzUfcDNw5lp3/XcQlLAmon8sdRlMYCpbdjwN7HnW7TULyBZiVM3XSg0jgWRSTgjhzyKipM/GWUCyKSfH5uXt6elBSUjLw7/Lychw+fBhRUVFIS0vDvffei5/85CfIyclBZmYmvv/97yMpKQnXXnutz/tyLiap+OqkaunTcM1r5V63v39pLDZmhQ8Jth/4qA476zx7jGRSGN68bhaCDPRBG8iELuHokTj+bzt6G0eepMMNwKz1RiQtnZlPUzS7BtWkwu7UoOkSFqMKzhmcp1ohPqqGONqCke5wDTflQbkoBUwd/7UgpYS2pRLay+6V2mNxQTB9azlgVqflCLyrtjnDkYO/RdHJ56Frw1fGMhhCMHv+5zBnwV0T20EP9f88xw4/jaKTz4+4kFFYeCYWLf0GElMumNLB7Kljz+Lw/l9hyOOqcyQmX4iVFz8OHmA3YGRi2Ww2lJeXIzMzE2az2atj9FebGcljy6/xS7nIbdu24eKLLx7y+p133olnnnkGUkr84Ac/wB//+Ed0dHRg5cqV+P3vf4/c3JH74ov3A6DgfdJpQuKqV8rQ7fBupvTTl6ZgbrR5UJ67JiTKOu340gc1cOju/3rvmheFz8+LooWaApjQJRzdEof+bIOz173fbeY6A1IuMMyoEXipC+gf10AvagcLMwIKB6xOiLJOyKYxyt4ZOMyPrgbzwXoHUhcQRe1w/O7gWLHOIDwnEqZvLB33+QORlBI7P3oI1RUfuLV/dt6NWHr+d/zcq/GTUocQOirL3kFV+fuw29rBuIqQ0GRk592IuIQl0yJ9REqB9rZCFB3/F6oqP4DQHYO2x8QtRE7+LUjLXA8AFLjPcL4KVoer8x5vCcV98y/xW513f6DgfZrQhcSTh1vwQmGHx20zw4x49srhJz3pQmJ3fR++90m9WwH81VlheHC599UEyMSQUuLA763oa/Hssl1wpxnhaXxajuSORNo02B76CPDwxlg5LwmGz85x+2ZnuMmmUkpXHW/OYP/1gSETYt1hemgFWErotPqdSSlw7NDTOH7kzx61W7z8AWTn3zQlAl8hnODnLMkrhD6tqq1IoQOMw+nsQUPtLjgdPVBUMyKjZyM8InNa3KQQ3/BVsAq4ykYebqlBi70XMaZgLIpJgTLFbg599X7Q1TXJOANuyo3Ai4UdHi9hcV1O+Ij56QpnOC8xCL9fm4Lff9qCg43WYY+RGKzitvxI3JAb4XnnyYQSukRHue5x4A4ANTudiMgY3wfnlGNSoF6ZBe0VD1aGDDFAvSrLNUo+RszcP++gu1agdq8TXVUCml1CNTGEpXFkXWaEarNCFHkeuAOAtq0ahs9OryodQjhRdPJ5j9udOvYP5My+xQ898r1zA3fXa9MncAcAdvrnMRpDkZqxbiB1qP/npMCd+IPCOJbEel4SeDqiK2ySMcaQGGLAf82OxHMn291uNzfajKuHyXU/m8IZciJN+O0lKajpduDVkk7UdDuhSYlIk4JL0kJxXmIQPMisIZOIKwx1e72r4d9WosPeJWAMZTMmfYYxBsOlGUCvE9r7FWM3CDHA9L+LwcKNY452C13C2iZx6j+2IfMOdLtE8zEdmWsE9AONHqXLDDrOoUYY75jrXeMAJISG8pI34XSOXrN5OH19jait3o6klJUUGAYYxjilxhAywehTMEDcvSgGdk3gJTdqvs+JNuEXa5LgztP0/uA+KcSAuxfFDMmNZ4xBnRmx3JQndIm2Ei+rE0mg5aSOxCUq2Ay76g3X5oClhkLbXAlZ2TXMDhzKknioV2W7AvcxqrwIXaK3SeDI323QR6nyqlo4RLdj5B3GYtchNeGTSbOBgHMVVeXved2+suzdIWUaCSFkJpphX+OB7RtL47A4PggvFnbgcPPQNJfkEAOuzQ7HjbnhUBgbsrrqaIabhErlIKcW3Q6vR3EBwNk3cx+xKIvioC5JgKjugn6gEbLHCagMPD4YynlJgFkZyFEfi3ACx54dPXAHAKlLsPHW2p9m16jN2up1W7vN+7aEEDKdUPAeYC5MDsbq1BBUdjmws64XPQ4Bs8qQF2nCsgRXigsF3TMTG2fa7EzONugfTWfJoVATQ1xBsYSrlnv/yLYbl5XQJeoPOOEco2ANADh6JUyxQd73Oco8rSarAq4FiiajLSGETCcz+Ot8/KSUENIVB/gqj7g/ME8NNeDGnIiB1/vPQSkuM5dqYjAEM7dLRJ4rKIZjpqemMs7OGc327IJiHKg/4N68g6YTAukXJAL/KQLsnqc7KRcmQ+piWi3WFByahM6OkjH3U1ULQsLSYDSGQNPssPY1ITgkEVLKGTNngxBCRkLBu4d0IcEZ0OMUONxkRY9TwKJyzI02IzZI9dnqpJwxTLMCBWScpJBIWKyi+uNhVgQdgyGYITpPmXYjuROtt0HA1u7ezVPDQQ0Zqw1QlidC/3js1TcHURjUVSnTKnAXQsOsnGtRV719xH3CI7KRnX8TMrKugMEweLlzh70LUgoagSeEzHgUvHtASomidjteLOzA1uoeOM9anZEzYEViEG7KjcCKxGAaISI+xzhD0jIV1TucHue+Jy6mS90X7N3uv/HOXonmExqi12dCP9QI9Lh/06VemgEETa+VcTlXkZy6CkFB8ejraxy0TVUtWHHRj5Gafgl6e+px4shfUV+zAw5HNxTFhPDIbOTk34z4xGVUQ5wQMuPRJ6AHnjnehj8fHb5ms5DArro+7KrrwxWZofj2inhXPi0F8MSHTKEcqSsNHo2+m6OYa4VVGnUfN0+fhpW970T4F0wwfnUxHE8edCuAVy5KgeHqbC97GNikFFiw5B7s/vj7A68ZDCG4+LKnERyajB1b70dt1bbTdcPP6O6qRE3lFoSGpWPZBd9DbNyigVrjhBAy00yfZ7J+JKXEP0YJ3M/1dnk3HtnTRIE78YvMS4xIWubefbc5kmHBHWYMs24M8YI5yrOPTEePxNHn7NAigmF8YAWU5YnACKUfWWIwDJ+ZA+Nts33R1UkldTHsvzlXkT7rCixc8jUArhrhF178OIJCErHl7btQU/nhkMD9bN1dldj2/ldQW/MxhPCybCohhLjhkUcewbJlyxAaGoq4uDhce+21KCwsnOxuAQCYlHLm1o9zg5QS9b0abn6jwuMqfb9YnYSlCUFUHYb4RdNRDbW7neiuGxrsqBYgYZGKtFVGcINrgSfiG4f/akVX9cgB5nCMoQyzbzAiLE0BrBq0nXWQjb2AJoBgA5SCeChZEVN+gqrUBcAY9MNNEEebIfucYAYFLDEY6kWpYGFG1z6coaZqK5obD2Lx8vux+e270NJ02O3zKIoJl238F0LD02mBIEICmM1mQ3l5OTIzM2E2j2+Vb10KHG5pRovNihizBYtiYqH48fq//PLLceutt2LZsmXQNA3f+c53cOzYMZw4cQLBwcFjH2AYvno/KG1mDALApqIOr8prv1TUgfOSvPsFEzKWmDkK4uar6KnX0VKoQ7NKcJUhOI4hdq7qqizjw0pIxFUqMmm5AV3VYxR5P4ejW8LRA0gB8CAD1DWpAGdgnA0EvACmbOAuT8//0T6ogPZRDdA59P3R3ikHXxALw/W5YJEmJKeuQnLqajQ3HvQocAcAXbfj5LG/Y/mFP/BF9wkhAW5rbRV+eeQAmqxn6vTGWYJw34IluDg5zS/nfPfddwf9+5lnnkFcXBwOHDiAVatW+eWc7pqa3xQTSAjgnbJhVmV0w56GPjT2el4ZhBB39I+mBydwpF1kQNZlRmRcYkDsPBVcdQWGFLj7FlcYYucoCE327KMzNJkjZrYy8DtjKh+Yg8AUPqXnI/Q/vHX84VNor5cOG7gDAISEONwE+2N7IOt6wCQH5wqKTr7g1Xmryt+H5uz1ttuEkClia20VHtrz8aDAHQCarH14aM/H2FpbNSH96OzsBABERUVNyPlGQ8H7GGp7HOh2evaIvJ+QwLFWGwRlJhE/YoyBK65gnSuMUmQmwLzbzQiOd+/jMzieY97t43tcHMgYY3A+ewLiaLN7DXqdsP/WNXlXCB21VVu9Oq+u21BdsRlCuFd3nxAy9ehS4JdHDoy6z6+OHIA+ylwZXxBC4N5778WFF16IefPm+fVc7qDgfQxWbXyBt1UTEBS7EzJtMM6gmoBFXzAjeYUKxTT8fooJSF6hYtEXzFBNmNKj6yORUkI090HfXedZwx4nnO9XgIFBNXifWmi1to46wZUQMrUdbmkeMuJ+rkZrHw63uDl44KWvfvWrOHbsGJ5//nm/nsddlPM+hmDD+O5vglUOGgglZHphnIEziVnrjchcZ0TjEQ1dVQK6Q0IxMoSlccQvmAHzDiSgbfXukbW+uw6Ga7ORmb0Rhcef9eoYjNP4EyHTWYvN6tP9vHHPPffgzTffxPbt25GSkuK383iCgvcxJIcYEGNR0GL1vCyZyoHF8UHT94ubkBmMMeYKzDkQv1BFQoHrNSmla2LqDLlr1/fUe9fQqkE/1Ij0jMu8Dt6DgxOp2gwh01iM2eLT/TwhpcT//u//4pVXXsG2bduQmZnp83N4iz713LBxVrhX7VanhCDcRAuJEDLdceXM5OD+OQgzgl0DrN7nnMsWG4KDE71qazSGISX9ElptlZBpbFFMLOIsQaPuE28JwqKYWJ+f+6tf/SqeffZZ/Otf/0JoaCgaGhrQ0NAAq9V/o/zuouB9DApnuD43fKR1VUZ1c14EdEp4J4RMV+NNN5cSBkOIV00zszeCMRoc8ZWRJv7ShGAymRTGcd+CJaPu840FS/xS7/2pp55CZ2cn1qxZg8TExIH/XnjBuwpZvkRDFm4INyn49vJ4/Hh3o9tt7pwbiXkxvn+MQwghAcOsAAoDdO8GKVioEYyrSMtcj6ry991uZzJHIn/enZQy4wNCaOBcRVPDARSffB5NjQegOfugqBbExM5Hdv7NSE5dBSkFPeUgk+Li5DQ8uuKiIXXe4y1B+IYf67wH8hqmdCW6QWEMl2WEAgAe2dsIbYzRpi/Mi8Jd86MnoGeEEDJ5mMKhLIqDfsD9gY0BKoOyJAFQGM5b+WM47F1oqNs9ZjOjKRxrLn0SJnMEzScaJyE0dHVW4JOtD6C7q3LQNs3Zi4a63Wio242g4AScv+r/EB07H5zT0w4y8S5OTsOqpJQJXWE1kDEZyLcWAUaXEp02Hf8p7sQbpZ1otZ2ZxBps4NiQEYob8yKQEmKgLxVCyLQnhYQo74TjF/s8bqssTYDh8/NOT/IVkFLi+OE/oKTwJdjtHUP2Z0xBUspKFCy/H0HB8TQK7AUpdIBxMMYghIaO9mJ8+O7/uLXYFecGrFr3a8QlLKX3nrjNZrOhvLwcmZmZMJun73oX7vLV+0HBuxf689irux3ocQoEGzhSQg0AGAxn1XLWhIQ6DWs7E0LI2ey/2AdR2uF+A5XB9K0VYEkhA/XvpZSna7ZLVJW/j7qa7XA4eqAoJkREZCE7/yZYgmIH0jyI+/rfs9bmYyg++QIiY+YgM3sj3nr5WthtbW4fR1WDcOX1r8AS5PvJgePVH8rQwFlgoeB9MF+9H/QJ6AXl9JdNRrjJtUiJBCSALVXd2Fvfhx6ngFllyIs0Y2NWGEIMHAKu9BtCCJlOpC5g/Moi2H++D7Jh7BFccAbDHfMGBe6AK+jqn4CalrkeGVlXDGwTQh9I16DA3TNCaOjqKMfuHQ+jo60QjKlYsOR/UVHyhkeBOwBoWh+KT76AeQVfCYj0mf6bko62IvT21ENCIjg4EZHReXSTR6Y1+sseB11KSAn85WgrXi/tQod9cC34zZU9+NORVlycFoJ7CmIQZlRoJJ4QMq0whUOaANMDy+H4xzGII82u0Yzh9o02w3DrbPDZ0aOuOHtu0BUIgeJUJISGtpYT2Pb+3dA010S/5NRVCAqOQ0nhS14ds7T4Vcwr+LIvu+kxITQIoaGs+DWUFG5CV0fZoO1h4ZnIzrsRs3KvBecGCuLJtEN/0V4SUkITEt/cVodDTSPX/HQIifcqunGoyYrfrU1GfJCBAnhCyLTiCuAZTF9aBNFmg/5RFfSjLZB9TjADB0sKgboqFXxONCDkqIE78R2HvQvbt3x9IHAHgJj4RehoL0FXZ7lXx7Tb2tDceAjxict81c0x9Y+i67odAIO1rwnb3r8bPd01w+7f1VmOg3sfR+GJ57Bm/VMIDkmkAJ5MK/TX7CXOGB7+pH7UwP1sTX0a7v2wFs9sSINKo0hkipBCAgwQTqCnUUC3SShmhpAEjv7vQsoxnVj9v5NAe9/7A3IWaYJ6TQ4M1+UO2i514erzTFnAapIJoaH45PNw2DsHvW40hsIxzIRgT9hsbZBS+L1UZ3/Q3li/D8UnX0Du7FsRGp6OzW/fBZu1Zcz2vT112PLOF7B+47Mwm6MogCfTBv0le0EXEqfabNhR60Z+51nqejW8VNSJ22dH0ug7CXhSSvS1SNTucaLpqAbhPLNNMQJxC1QkrzDAEh14geR00h+sw6ZDlLZDWjUwkwqeHgYWbnIFxUrglEtjjLn6e+7rAdTHmYGhtPjVIa+6AmLDuI7sau/fa14IDXZ7Jz764B50tBUiPCIbCcnnYfuWe90K3PvZrC3Yt/MnWL3uN37sLSETi4J3L3AGbCrqHHvHYbxW0onPzon0cY8I8b2qj5yo/Mg57DbdAdTv11C/X0PGWgPSVhonuHczg5QSsq4H2oeVrlrqzrMWmeAMfG4M1ItToeRHQ0pJN1FkQFPD/mGDXJu1FcGpq8CYAin1YVqOLSw8w69/a0JocNi78MGbd6Cvtx4AkJ1/I3p76lFfs8Pj4zXU7kRfbyMsQXF0jZBpgYZCvGDTJbZV93jVtrFPw4FGKwRV6CQBSkqJim2OEQP3c1VscaJqhyOgV6ObqrT3K2B/ZDf03fWDA3cAEBLiaDMcvzkIxz+PAzKwVwQkE0cIHda+5mG3VVd8AEtQLBJTLvTq2FExcxEeMWs83RsT5yp2fvTQQODOFSMysq5EaeF/TpcT9YyUAkUnn/eqLSGBiIJ3L7RZNTiF91+Std1OjKM5IX4jhURvg0CVm4F7v4otTlhbJQWPPiKlhHNzBbTXSkas3HI2fVcdnM+eoFFFMmCkfPTOjlI0NRxATv7NXh03J/9mCKGNp2ujklKc7uP+gdfM5mgYDMFoaf7U6+O2Nh+lqkXEIz/84Q9Pl7A9819+fv5kdwsABe9eGW/grUvpzvcxIROPAbV7vPtirt3jWcBPhielhGy3Q3ul2KN2+u466MdbIHUaXfRGf0CqaVZYrS1w2LshpfBroOovnCsICU0ZcXvxqReRmHwBElNWenTc6Nj5SJ+1we8TP4tOPD/o36rBAgBwOvuG290tTjdWkSWBTZcSB5ra8H5VAw40tUGfgMGiuXPnor6+fuC/HTs8T9vyB8p590K4eXx371FmhQoukICkO4Dm494FK01HNMxab4QyvrlwRAL6tiq3RtzPpW2tgmluDOW/e6A/OK+u2IziUy+ipenwwDZLUByycq9Hdv6NMJki/F5dxZdi4hYgNCwd3V2VQ7ZVV2xGZfl7uGD1o/h4y72DRrlHEhk9G6vX/Rb+nqjKGB+S166dDtoNhiCvj2swBI+rX2Ryba1pwq8OF6LJah94Lc5iwjcW5eHilDi/nVdVVSQkJPjt+N6aOp9EASTMqGBxvMWrtiEGjguSg8Hpi5UEoO5aAW8HGnUH0NtIo77jJiS0XXXeNT3ZCtHqXvla4grcO9tL8cZLV2HX9u8MCtwBwNrXhGOHn8ZrL1yOU8f+MTmd9JIQGrLzbhxhq8SeHT9AU/0+rL70ScwvuBuWoOEDIJM5EnPm34W1G/4C1RA8IaknDkfXoH/brK1wOLoRG7/Y62PGxhdACO8m6JLJtbWmCd/edWRQ4A4ATVY7vr3rCLbWNPnt3MXFxUhKSsKsWbNw++23o6qqym/n8gSNvHtBExI35kTgYKPnX5KXZ4bCQGUiSQCSUkKzje8x5HjbE0C2WoFeL1OQJCDKO6FEmKie+hj6A/ct73xh0CJGw5FSw6cHfgNNs2Heoi9NiacanKvIyrsexadeRE939ZDtQndgx9ZvYu7C/0HO7Fswe/7nUFu9Hc0NB+B09kJVLYiOXYC0zEsBsAnNF1cUEzTtzPerEE5UlLyJrNzrceLIXz2uksOYgpz8mynnfQrSpcSvDheOus8ThwuxKjkWio+vyxUrVuCZZ55BXl4e6uvr8aMf/QgXXXQRjh07htDQUJ+ey1MUvHtB5QwXpQQjM8yI8i6H2+0sKsOt+VQmkgQmxhhU8/g+/FRT4Ac1Ac8+ztFBuw7Z4wBCjFRbfQzbt3xtzMD9bMc//SPiEhYjNn7xlFjwh3MDLr7sKWx5578HKrecTUqBY4efxsmjf0Na5nrkzrkdyYPKSMpJ+TnDI3PQ1LBv0GslhS8hd85tSE5bjZrKDz06XlLqRbAExfqyi2SCHG5uHzLifq5Gqx2Hm9uxJC7Kp+fesGHDwP9fsGABVqxYgfT0dLz44ov4whe+4NNzeYo+2b0kJPDLi5MRF+TeB5uRM/x0ZSJiLSqlzJCAFZp0ZuVUT3EDEBwfeB8pUkpIXUydSjjmcQZLZgWyy+FakIIMSwgNVeUfjFhOcTSlha+Ac3UgV95qbUVnRzl6umuh6w5IKbwuSSilgK47oOtO6Lpj3GkenKuwBMVh/cZnkT7rCnBl+PUYODdANQQjLCwdgKuqBufquBdz8oYQGnLybxryeldnOWqrt2PJigcRHJLo9vGCghOx7ILvU8rMFNVqc2+A1N39xiMiIgK5ubkoKSnx+7nGEvhDBwFK5QxRZgV/uSwVP9zZgAOjpNCkhhrw3RXxmBNthkJfqCSAKSYgZo6KpiOeJ77HL1AxCd/1w+pfdVR2OSBK2iFtGphZBc+KOL0qqQQL0LQSFmMBwk1A5+ijTcPiDEpWBPTKLrDUMN93bprgXEXxqRc8bhcRmYNFy+6FrtlRXvomSgo3oaOtaGC7aghGRtaVyJ19G8LC090+rmvVUxWdHWWor/kETmcPFMWM6Ni5iE9cASl1r0fAOVdhMobhvIt+jCUrvoXSopfR2nIcmmaFwRCM+MRlyMi6CsrpwH6yJ+VyriI57WKYLTFDFpna8/HDWHvF33DJ5X/CRx/8L7o6y0c9Vmh4Bi5e/xSMxlBKmZmios3uLQDo7n7j0dPTg9LSUnz2s5/1+7nGQsH7OKicIcyo4DeXpKCqy4FNRR3YW9+HHqeARWXIjTThhtwILIkPgiYkBe4k8EkgeYV3wXvScoOrQkoA/JmLsk5oW6sgjjQPru3KGfi8GKgXp0HJ8+0jVp+REuqFydDeLvO4KV8QCxZhhthSCSz0XwWGqc7p7EVr8xGP2oRHZOGSDX9GV0c53nvjv2C3tQ/ZR3P2ouTUiyg59SJyZ9+GguX3u5UfX12xGUUn/43W5qNDtoWEpiAr70bkzr4NjHGvglB2uo3RFIa8uZ8ZdCPQf+MQWCSWnf9d7Nj6zUFPMRyOLmx973+wau1vcNnV/0JV+XsoPrUJbS3HB7WOjJ6NnPxbkD5rw8BTBDI1LYqNRJzFNGrqTLzFhEWxvk9Jvv/++7Fx40akp6ejrq4OP/jBD6AoCm677Tafn8tTTE6ZZ8mBrX/F1HNTYnQK2skUVL7Fgeod7k+aTL/YgLSLDAExkc/xUiH0D8euCKCsSoHhlvyA6PO5ZJcDtu9uB3TPPp6N9y5xrbxa0wP14tRRc9513Yn+epSuoHDqBDiugM6V3iF0JxjjYFxxOxDt623C65sud/t8qmrBhmtfgs3agg/f/RJ03eZWu1k512H5hd8fcbuUEvt2/hhlxa+Oeazo2AVYc+nvoKiWGTGKLKVAZdk72PvJj4bU2ldUs6uEZ95NCAtPR1dnBWx9LZAAgoLjERqWGqA3JTOPzWZDeXk5MjMzYTabvTpGf7WZkTxy/gK/lIu89dZbsX37drS2tiI2NhYrV67ET3/6U2RlZXl9TF+8HwAF74SQEbgbwKevMSB9tf8fWbrD+XIRtM1D61qPRFmTCuPNgbFi3tmkkND31cP59+Nj73yaemk6DNflwv7UIRg/Nx/MMjRw6Q9orNZW1FRsgd3eAc5VhIZnIDl1NQAEdGAohA7OFbS1nETRyX+jpnLLQFWS4JAkzMq5Dtn5N8JoDBv1psxu68Arz1/i9nmzcm/A4hXfwpv/ucrjPPnzVz+C1PS1wwaSB/c8jqKT/3b7WDFxi3DJ5X+cMUGplDo62ktx6tjfUV2xGUIM/jwymaOxcMk9SMu8HIpimPSUHzKUr4LV4eq8x1tMuNfPdd59jYJ3Qojf9dTrqN2jofm4Nqj+O1eBuPkqkparCI7nkz56LYWErOmG/dE9Hrc13r8MPCMcLMCekEkpoe+uh/NfJ8YcgVcvz4Th6mw4XyuG7LTD8Nm5Q34nUgq0Nh/FyWP/QF319iHl9syWaMzKuRb58+6AqloCLkAUQoPm7MOOrd9EU8OBEffj3IDZ8z+P+QVfHnEfKQVee/Ey2Kytbp37squfR1dHGXZt/47H/Y6OXYBLr3xmyPm7u6rw9ivXe3y8Zed/D5k5Vwfc78dfhNDBGIfT0Y3qyi2w29rBuIqQ0BQkp67CRJexJJ7xVbAKuMpGHm5uR6vNgWizEYtiI31eHtLffPV+zIyrnxDileB4jtxrjMi63IieegHNJqGaGUKSOPoLV0x24O7qhGt1UW/o26rBPx/u4w75gJBQlieA50ZC31ED7ZNaoOeskUeTAmV5ItRVKeDJoXC+XAT9YCNMD64YMvdASoniU5twaO/jI1ZCsVlbceLIX1BZ9i4uvuxpBAXHB0yAKIQOzdmHD966A91do/+ehXDi+Kd/hMPeiSXnPTjsPlJKzMq5FieO/GXMc0fHLkBkVC4O7n7Mq763Nh9BR3sJwiNmDRoZLjrh/oj72YoLX0RWnudB/1TFz8rXz8y+aqBq1FRL9SLjpzDm83KQUxX95RNCRtQ/Gq2agYhMBVLKwAjWz2XToR9s9KqpfrgRhr48yCA1oB67M4VDCgkwQL0sE+qVWZC1Pa7KOSYFLD4YMHLoB5vg/PdJyG4HjPcuBYLUQU8RpNBRUfYODu5xL/js7anF1vf+B+uveg5GU5jb70l/X/3x98G5gp0fPTRm4H624lMvICpmLtJnXT4kyONcQU7+LTh59JkxF/yJiMyGrtvR3HTIq74DQH3NJwgNSxuo6CKEhoqyt706VkdbEdpaTiIyOjDna/jTZJSuJCQQBc43FSEk4AVqsCDqegCnd7W1oUno1Z2+7ZCPMM5cpS17ndAPN0FUdkK2WSEqOqG9Uwbbdz+G9lox+PxYmL59HlikacgkVSF1HNz7M4/O29tTj2OH/zDqPlJKV7qSLqCfaoW+vwH6wUaImm7Xdt3L38eQ8wh0dpShoW63x20Lj/9zxNFZsyUa8xZ9acxjGAwhcDp6PT732ZzOHrCzHoVY+5qgOb0/Zkd7kcerjBJCpg8aeSeETH02z0tbno3ZJKx9LTBbogLuUTxTOBBlhhJpBnQBcawF0inA4oJg/Pw88NwoQMhhK8sIoaGi9C04HT0en7ei9C0sXPp1qOrQvEypC6DHCW1rFbSd56TzAGApoVBXpUA5L8k1Gj/OlV49mdR5to72YrQ0HUFUzNwhedGMMcxd+EVomhUnj/5txGNomhXKMO+BJ1TVAokz8xZ03QFFNYMzFU5nLwDPpp7puh2g6WqEzFiB9S1FCBlE6BJcYRCahGYHuOJaSAkSY06w1ISAygcHTdO2dGnQ+B6nSwtDZ3sxgoIv8FGHfIsx5sph5wr4wjhX7XoGgLvKJWKEBac4V1FS+JJX53Q6e1BZ9g4ys64CV868v1JIiOJ2OP74KWAbfvRX1nTD+a+T0D6ugemexa6UJC8DeMY4aqs+8qotAFRXbkZUzJwRty9c8r+IS1iCwuPPnR7dHxwUC+GEwRCE0LB0dHe5X8nobFExc8CY6+ZBSonQsHTc9Jmdp4+vo6XpEIpPbUJN5VZIOfaNqNEYBgRQihchZGJR8E5IABK6BONAW4mO+r0a2svOBEmGYCChwICk5SpMoeemSEhwxtBpd+KVshpsr21Gl8MJg8KRERqM67NSsCw+atjAfirjKaGARQWsXozAmxTwjHA0HduP+MRlgwLVQMQ4Azy4AevsKPX6XJ3tJcBZqVJSFxCVXXD8/jCgjZ0WI6u7YX9iP0zfWg7Zf6PhBYe9w6t2rradA4HzSOITlyMx+QL09tSjtno7nI5uKIoJkVF5iE9aDiE0ZOfdiEP7fuHx+UNCU5CQdN5ArfKe7hqUFv4HPT01kEKHyRyJtIxLceGax2Dta8aRg0+ivOT1EY+nKCYkpqykCiuEzGAUvBMSYIQuYe+UOPacDda2oY/Gnb1A9Q4nqj9xInWlAZmXuCbB6UJAkxKPHyzEe1X1cIrBbcu7erG1tgmpIRZ8syAPy+OjhywqNmUpDMp5idC3VnvedHkioHKUFb+O/Hl3wqQEYOUZL0kpIHSH1+113T54wipncP7tmFuB+0Af6nvhfKMUhutzvV59l3PjkIV6PGkLKYBRAvj+VKmg4ARk5V6PM4tXKQPbZ+VehyOHnoSuubdAU7/5i++BEDraW0/h0wO/RVPDviH7lBW/itCwNOTPuxMrVv4QQcEJOP7pH4c9XmrGOhgMwR71gRAyvUyfoTdCpgGhS9i7JA7/xTps4D6IBKo/dqLkHdeiFboEvrL1AN6sqBsSuJ+tuseKb+74FJurG6FPl7xZBqhr0jwake5vx1cnobpiM+y2NiiKyT/9mySMcRiMIV63NxhDIYXrqY/UBcSJVshWq8fH0XfVebxa7NlCw9O9bhsWnjFiecxzMcagKAYoihGKYhw0uq2qZixZ8ZBH507Pugqp6WtRX/sJtrz7xWED937dXVXYt/PHOLj355hf8GVk5988TP848uZ8xnUzQgiZsSh4JySAcIXh+L9tcPa536Zur4amYxo6bU6caO9yq40uJf7f3uMoau+GLqZ+IMAYA4u2QL05z6N26vU5UBJCUHjiWYSGpQ07OXMqk1IiKeUir9unpK0ZyK1mCof2kedPNgAAVg36vnpID0bs+wmhITv3Bq9OqygmZOZc45NUKMY4MrM3YvGKB90qnxkTtxDLL/geOtoKsXPbg24/ASk68S+cPPoMCpZ9Eybz4JrWi5d/CxFROWCUMkPIjEbBOyEBQgqJ9jINfc2ej1DW7nYiNtiExbGRbrfRpMQ/TlVAmSa574wzGFalQr01b+wReAaoN+XCsDYDe3f+BG2tJ5CVd6PXqRmBSkqB3Nm3etU2PDIHMXELB40+iwrvS2qKik7Pn4zAlbKSkX2lV08Q0jLX+zTFhDGGnPybsP6qZ5Ex60rw/pXKzhIRmYOl538Xl1z+RyiKEUcO/d5VHcYDx4/8BUI4MCvnGgCAopqxYuWPkJ1/U0CtRUDIdLZ9+3Zs3LgRSUlJYIzh1VdfHdjmdDrx4IMPYv78+QgODkZSUhLuuOMO1NXVTUjfKOedkADBOEPdXu+Cx+5agc56DTdkpeBgc7vb7bbXNaPN5kCUeWgQMhUJoUNdmQw+Lwpie/3QMobBBqgXJIGvSgKLMGP3xw+jovRNKIoZWbnXBVyZyPHiXEF07HxExcxFW8txj9rmzb4NQmiD3xP7OGqL2/UxKySNhHMVi5bei307f+J2G5M5EvML7saQ5WbHiTGOiMgcrLjo/2Hxed9Cfc0OOOzdUBQjIqJyERUzB0JoYExBb089Gmp3eXwOzdmLitK3kJN/C8yWGMzKuQaqag7YdRYImQi6kPi02YpWq45oi4KFsRa/Vk/r7e3FwoULcdddd+H66wevatzX14eDBw/i+9//PhYuXIj29nZ8/etfx9VXX439+/f7rU/9ptc3FSFTXFux98FRR5HAnIIwj9roUuK9qnrcmJUKwzhrcQcCzhXouhN1XXuReMUFMF+VCb2px1XS0KyAx4ZAwrW6ZdHHz6OzowQAsGTFt6CqlknuvX8IoWHlJb/A+298BjZri1ttMrOvwazca4duMKuA08sJsGYVUkivAnjGFGTlXg+HvQufHvgtxqqLbjJH4eL1T8FsifbLSHV/2orRGIrUjPWnJ8SyQRNchdBQVvTKmH0dSVnxa8jJvxk5+TdNu5tKQjy1rboHTxxoRvNZFcViLSruXRKLNanez+sZzYYNG7Bhw4Zht4WHh+ODDz4Y9Nrvfvc7LF++HFVVVUhLS/NLn/rRJwIhAUJ3ynHNQ9NsEpFGzy/pVpvDlwOTk44xjoSk5fh4230ICUly5bIbgqH19aKnsgaV5e8OLFrEGEfB8vuRmXPNtB3V5FyF2RyF9Vf9Ex998L8DNyzDYYwjd85/YdHSb0BKOeQ9UXIjoR9o9K4feZGnFxby/n3On3cnIqLycOrY39FYv3fIdlUNQkbWFZiz8IswmydmwS1XWtHQHHTOVfT0eP8Ivfd0WwrcyUy3rboH391RP+T1ZquG7+6ox09XJvotgPdEZ2cnGGOIiIjw+7noU4GQADHeOWjcwODwYkn66Raycq4AqgWr1/0G5cWvo/jUC+hoLx68j2JEavo65M29HZFRedM2cO/HuQqzJRobrn0R9bW7UHzyBdTX7hiowmIyRSAz5xrkzr4VQcHxwx5DCgllTZp3wXuYEcqieK/TZvoxxhCfuAyJyeeju6saVeXvw27vAOcqQkNTkZ51BZTTeeiBkBsupfdP0sbTlpDpQhcSTxxoHnWfXx9sxkXJwZO6AKHNZsODDz6I2267DWFhnj0B9wYF74QECMYZLNEM1lbvHrMHxTI0WT2rQQ0ACUHTq8IKgIFJlpk5VyMr73q0thxHR1sRdN0BozEUSSkrYTCGAlIERJA3EfpHcPuDX6E74XT2gnMVqiHo9Ej7yO8F4wxKVgRYcghkbY9H51ZXpox71L1f/88REpqC/Hl3uG68pGvFWc4DZ4EtIXSYLdFetzebvW9LyHTxabN1UKrMcJr6NHzabMXi+KAJ6tVgTqcTN998M6SUeOqppybknDPjW4uQKUDoEolLvQs+DEFAzBwVb1V59pjexDkuT0+EYZpUnDlXf6AXFT0HmdkbkZV7PdIyL4PRFOYqLzkDS+71vydcMcBkjoDBGALGODhXxnwCIXUB438vcK1m6+75siOgbsgE8/Gciv6a7Jyr4IohoAJ3wHWbkjHrCq/bp2VeNu2qHxHiqVare0+g3N3P1/oD98rKSnzwwQcTMuoOUPBOSMDgCkNigQpvYpCExQY4hMA7lUPzAkezNjUeQer0D2AZY+BcPR3sTf+f11+YwsGiLTB9cxkQPvaCVnxONIz3LAameVrScBhXEBUzB5HRsz1vy1TkzL6Z8t3JjBdtce/z2t39fKk/cC8uLsbmzZsRHT1xT8soeCckgHAjMGu9Z2UbLdEMqSsNONHeiT7N/dGHIFXB52dnelkLY/qSw8wbGO61mYopHCw+COYfXQjDbbPBks+ZKMYZ+IJYGL++BKZ7FgMqH3eu+1QlhIY58+/yuF1m9pUwn7NAEyEz0cJYC2LHeNIXF6RiYazvq4X19PTg8OHDOHz4MACgvLwchw8fRlVVFZxOJ2688Ubs378fzz33HHRdR0NDAxoaGuBweFmRywNMyumyPjoh00f1Jw6Ub3aOuZ8lhmHBZ80wBDNIBjy081N8XD92OUCLouDnKxdiYUwE1GmaMuMpKSQgJfQDjdAPNEB2OcAUBhZlgbIyGUpuFKQufJ7+MZX1vx+ipQ+y2+EK7KPMYCFGeq9Ok1LixJE/4+gh93Jh4xKWYc36J8HY2GlMhAQ6m82G8vJyZGZmwmz2bn7VSNVm+vmr2sy2bdtw8cUXD3n9zjvvxA9/+ENkZmYO227r1q1Ys2bNsNt88X4AFLwTEpCklOisFKjZ5XTVfj/nKjWGMiQuUZF8ngFcdaXciNOX8l9PlOM/pdVotw8f/C+Li8LXF+YgMyxkUmfnB4r+kojO98qhbakcvKjTWVh8EAzX5YDPj6WginistOhlHDnwO9jtHcNu59yAzOyrseS8B8EwM+djkOnHV8HqcHXe44JUfH2x/+q8+wMF74RMc/0L2ti7BFpO6dCsElwFgmI4onJcX+zDpSPoQkJC4sOaJmyrbUKXwwkj58gIC8YNWSlIDgmCJgSNuJ/F8fdj0Pe4MV+AAYZb86FcmDJjU0GId4TQAUhUlX+AsuKX0dNdCyE0mM2RSM1Yj+z8G2E0uia70c0hmS58FawCE7/Cqj9Q8E7IDCGlxKCSzxzgbnxgnRugCynBQIHB2aSU0N4ohfZuufuNGGD8agF4XhSlhRCPCaENmYg63GuETAe+DN6nA1+9H/TNQ0iAY4yBq2f95+ZIw7kj65wxCtzP5dChfVjpWRsJaG+VUeBOvDJckE6BOyHEE/TtQwiZkaQuoO+sAxyeV5IR5Z0Qdd2uSa6EEELIBKLgnZAAJnXX5Bxp74PeUQ/R2QipOSClgBRUvnA8mMKhuZPnPgJtp2cLYhFCCCG+QM/qCAlAUmgAU6CV7oZ9zwvQSnadXl4egMEM47xLYTzvNqiJeZBSjLqsPRmZ7LB537jDTpNWCSGETDgK3gkJMFLXIK2d6P3HPdAbiobu4LTBcegNOA69AcPcdQi64ceQXAWj6jGeG0fWy0hz/aUmAM4ABkBIgDEK8gkhhPgMBe+EBBCpa5C2bvT88U6IjrFTOpzHN6PX1o3gz/52Ano3/bAIE2S3d6vhsQjzQDnPgf/tcUDbWQvZbAV0CRZqgLI0ASw1jBYtIoQQ4hMUvBMSQJiiovel77kVuPfTSvfA/tGfYVr9RVrYxQNSF1CXJ8JZ3e1Ve/WCpIHjyBYrnG+WQj/cBOiDR+S1DyrB0sNgWJ8BpSB+3P0OJOeWOHQ9jZCUxjVD9f896LoDQndAUS3gXKFSmIT4GF1NhEwAXUgonMGhC/Q6NSicIdjguvw4XOUgpRQQbbXQSnd7fHz7vv/AtOoLPu719MYUDuXCZDjfKPG44gzLCANPDoXUBUR5Jxy/PwzYtBH3l5VdcPzpCNQNs2DYmDXOnk++/gWHqiu2oLryA9htHeDcgNCwNGTn34SIyGwK2GYQKQV0zYbS4ldRWvgfdHWeWTchLmEpcvJvQUr6Gkgp6W+CEB+gq4gQP+pfKOlAcxs2FVfjk/oW9IeJoQYVV2Qk4pbsNCSFWAAJ2Hf/26vzyJ5WOE9sgWHOWjCFLmu3mRSoF6dBe6/C/TYMMFyZBakJyFbrmIH72bR3ylypNKtSp2QevJQCAMOpY39H4Yl/wW5rG7S9sX4PSgo3ITp2PhYtuw/RMfPA6WnQtCalREXpW9i/+xHo2tAJ4E0N+9HUsB9BwQm4aO0TCI+YRQE8mRJ0XccPf/hDPPvss2hoaEBSUhI+97nP4Xvf+96kr5lCzzYJ8RNNCHTYnbjjgz342vZD+PiswB0Aup0aXiiuxg3vfIJ/nqoA49xVVcZLzqJPAAqUPMIYg3p1NpSlCW42AAw354HPiQZTOZyvl7gduPdzvl4K6JNX5lOedW7X6r2e9WXnR9/GkYO/GxK4n621+Si2vvs/qKvefnqUfnRCDP8ejvQ6CQxSCpQUvoQ9O34wbOB+tr7eBmx5+/PobC+h3yvxihASFbU6jhVrqKjVIfy8zsZjjz2Gp556Cr/73e9w8uRJPPbYY/jZz36G3/528ueY0e0vIX6gCYEuh4YvbNmLRqt91H0lgANN7fhsfgakzbv8awCQtu5JHw2Yqox3zYczMRjah1VAr3PYfVhcEAzX5oAvjHWlOXU7ID5t9vxkNg36nnoo5ydN2ARWKaSr+k2fBm1HDWR9D6RDgAUZoMyPAZ8fC0g5Zn8O7/sVqived+ucQjixc/u3se6KZxARmT3saKsQGhjjqK/dieJTL6Kl8RA0zQpVtSAmvgA5+TcjKWUlpBQ0WhtghNDR0V6Eg3sec7uNplnx0eavYeONb/qxZ2Q6Olmm4b1PHOjqPROwhwUzXHahEbNn+eezYefOnbjmmmtw5ZVXAgAyMjLw73//G3v37vXL+TxBn4aE+IHKOb6768iYgXs/m356dFI1eX1OZjB73XYm67/hUS/LhHppBvQDDdD3N7qq0HCAR1ugrEyBkhflqhjDGKQmoH1S6yoF6en54oIA05knJFLIgcC5f8VWX6bUSF1AdjmgvVIM/XAjoA3us76zFizSBGV1KtRLM1znH+Ym0G7rQPGpFzw6t9AdOH74D7ho7a+GbhM62ltPYedHD6K3Z/AEbU2zoqF2JxpqdyI4JBHnr34UUdFzKAUngDDGUXj82dOpVO6zWVtQWfYOMrKupBsy4paTZRo2vT/0u7SrV2LT+3bctB5+CeAvuOAC/PGPf0RRURFyc3Px6aefYseOHfjlL3/p83N5iq4cQnxMSInyrl4caulwu01jn+uRs5KYB62zwavzKol5kLpGOe9eYpwBnEFZmgB1RdKgbf2pJQMj0wqDbO7z+BzqlbNc+fIddmjvlkPfWedaKEoCsKiuc1+cBpYQPFB+cjykLiAb+2D/9QFglJKYst0O7dUSiPJOGP97ISTkoABeCA0lhZu8Sneoq/kYfX3NCAqKHXS8lqYj+OiDr0LXR7/B7e2px9Z3/werL/0dYuIWUsDnI0MrBZ3+G3ezUpDT0Y3qis1enbv41IuYlXONV23JzCKExHufjF7O971PHMjLUMB9PI/ooYceQldXF/Lz86EoCnRdx09/+lPcfvvtPj2PN+hTkBA/eLG4yqP96/tsONLUirxlN0E79ZHnJ1QMMC69ngJ3HxgudeTc1xhjQ0pCjsVwUx6U1alwvlzkSs85d9TeqkH/uAb6xzXg82NgvGs+pMq9Tq2RUgK9Tth/e3DUwP1s4tNmOP91EsbPzBn0Oucqyku8S3WQUqC8+DXMnncnuGIAADidvfj4w3vHDNz76bodH3/4DVx1wxswmcK96kegE0IHY2zY4FkIJzg3DPy7f+KwN2ly/alKDbW7UFn+Huy2NjCmIDg0Gdl5N7pVKUgIHXU1OyDE8ClmY2lvPQmbtQ1mS5RX7cnMUVUvBqXKDKerV6KqXiAj2bdP5l588UU899xz+Ne//oW5c+fi8OHDuPfee5GUlIQ777zTp+fyFH3TE+JjnDF8Ut/icbsXy+rwk/POhz0yBaK9xqO2hrnrwMyhHp+TeEfqAggxjL3jacqaVKgXp8Hx92PQ94xdw18cbYH9l/th+uYySOblCLx01ZhHp3sBcj99Zy3EZRlgMZZBwWFfr/trD5yrr7dxICgVQkPJqU1wOno8OobT0YOSU5swe/7nps3oe/9k3ubGA7D2tYAxjtCwNETFzBkI5oVwoqLkLdTVfAyHoxuKYkJ4ZBZy8m9BSGiyxyU5Swtfxqnjfx+SqgQAJadeRHTsfCxc8jXExi0acd0IKQUc9k7vfujTHI4uCt7JmHr63BskcXc/TzzwwAN46KGHcOuttwIA5s+fj8rKSjzyyCMUvBMyHXU7PE8v2FbThNbePoRe8330/fOrgO7eMVhoDCyXfR2QAmCBkRMshQDY4Efw0yqlhzEoSxKgf+jGExaVw7BhFrStVW4F7v1kdTec/zwO4xcWeNdHIaHtrvOqqbatCoYb8lyTXH1CAqdvBBjjKC162aujlBb+B3MW3OWrTk0aITQ47F0oPvUCSotegc165mafcxWrL30SsXGLcPjA71BW/MqQG52Gul0oPP4c4hOXY/GKbyE0LNWtAH7/rv9DSeFLo+7T2nwUW9/7Cs676MdIy1wHNsxnCmMMiuL9/BwA425PZoaQIPc+hNzdzxN9fX3gfPCTMEVRIMTkVQvrR6UiCfEDkxepDpqU+NbuY+DpixB86+OAGxNQWXgCQj7/R7CgyIBYXVWevuEQrZWwvvUz9Pz1f9D9xzvR88+vwXHwVUinDVKI0ytxTl2MMyiZ4WApIWPuqyyOA4IN0LZUenwe/VATpIcj54DryYB+uGnEyjljnnf30JsMsyXGq2P1t5WnR5nbWwvR1+vdvI6+vka0tZz0qE3/6LbT2Yuuzgp0dpTBbms/vW3iSxYKoaG15TjefuV6HP/0T4MCdwBYdsH3ER07D9s+uBuFx/85yhMKicb6Pdj81p1oazk+6s8ipcDxT/88ZuB+Zn8Ne3Z8Hy1NR4c9LmMc4ZHZbh1rOAZDCMyWaK/bk5kjLZEjLHj0wDwsmCEt0ffh7MaNG/HTn/4Ub731FioqKvDKK6/gl7/8Ja677jqfn8tT02QYjJDAkhUe4tGE1X7H27rwcX0r1uRciNC7/w37J/+E48g7gMM6aD8WEgPj0utgOv82MGNwQIxoS6FDbyqF9e2fQa88PGS7VvwJrO//BqYl18F86f9CMhYQNxzekrqAemkGnH87Nup+yqpUiOMtkG2j18EelpDQtlVBvSobTPFgZEkCssU69n4jsWqAXQMsrtQgIXSkz9qAk0f/5sXBGDKzN4KdHhm22Vq97xcA2yj15c8mpQBjHC1Nn6L45POoqdoGKc8EorHxi5Ez+xakpF0CYGJW/hRCQ2d7KT56/25o2tDfT2z8YmRmb8QnW7+FpoYDbh3T6ezB9s1fx2VXP4+g4Lhhc+Z13Y6TR5/xuK9HDz2FSy7/w5BtjHHExC1AeEQWOjtKPTouAKo0Q9zGuasc5HDVZvpddqHR55NVAeC3v/0tvv/97+Puu+9GU1MTkpKS8KUvfQkPP/ywz8/lKbp6CPExTQhcl5XiVfBuVjhWxEeDcQU8MgWWjd+G5fL74Dy5FaLXNbGMRyVDzbkQAMC4Aqk7XaOajAOnRxknOpiXQoNWeRi9z34dcI4SpNp7Yd/5LPT6Uwj+7G9dAbyb1S0CDVM41GWJkDXdrtzyYXcCeEY4nP864fV59E+bYbgmx/OG413A5KwJuYxx5My+FaeO/d3j0oDxicsREpo88G/OjePqlqKM3V4IHUI4sWPr/Wio3TnsPs2NB9HceBCh4RlYs/73sFhi/B5Qcq5i98ffHzZwB4Cc/JvR3laI6krPqrg4HF04dezvWLzigSHbhNBQVvw6NM3z6khNDfvQ3VWFkNCUIdepEBqy827CgT2Penzc3Dm3edyGzFyzZ6m4aT0mvM57aGgonnjiCTzxxBN+Of54UPBOiI+pnOOSlDg8cdiINrt7VT76XZqaALPqGo1m/bl2RgsM89a7ctrBAM7Rn4wsHVY4Dr0BvbEEUrODW0Kh5q2CYdbyCcsxl0JAtNej91/3jR64n0Ur34++lx9G8M2ef/EHGsN1uUCIEdrbZYD9nNVETQoYZ5Bepq8AgOzx7G+oHwsdR5DMGWA587fDGENQUCwysq5Cecnr7veBKZi78IuDJlWGhafB9ffr3c1FaFj6qNullIAU2Pb+V9DS9OmYx+vurMDmt+7E+queg8kc4bcAXggdrc1H0dlRMux2syUGKekX48Bu9xc9OltF6VtYuPTrUNXB6XauSkHu/87OVVr0ChYsvgfnFrbhXMWs3OtQUfYWWpuPun283Nm3ITQszev+kJlp9iwVeRkKquoFevokQoJcqTL+GHGfCqbmkBchAY6B4VuL8z2a7xdrMeHL87OGP56igqlGMNUASAlp7YT1zUfQ+bNLYX3rMTj2/wfOw2/Cvuvf6H3mK+j69XVw7P8PAPg/v5wB9u1/Bey9HjVzHvsAekuFa3LrFKeuTYf50dUw3JwHlhziWoRJZQMVacazkiozeN6WqdyVa69698XGF8YO6bOUEssu+B7iE5e71wfGsfT87yA2vmBQQBwUnID4xGVe9Ss+cTmCQxJG3UdKgSOHfu9W4N7P2teMXdu/69eRd84VFJ96fsTtickXAAAqy97x6vhOZw+qKzYPm6Pu7RyD/rYjLY7FGMfqS3+HqOg5w24/16yca1Gw/H6v+0JmNs4ZMpIVzMtRkZHs+7ruUwkF74T4gcIZViXH4rtL50BxoxZzQpAZv1+9BOFGA/go+0tdg+hsQPfTn4Fj/8tDcuH7idYqWN/6GXo3fQfejnC6zWGF49j7XjW173rehxVNJg/jDMykQLkoBebvng/Lry6B5TfrYPl/F7lWZY0P9v7Ycd61ZcFGKIvivWqrXpw2sDDVwPEYAwPD6kt/h9zZt0FRR55QHRKaipUX/wKzcq4ZNt0iJ/9Wr/qVk3/LmJNMpdRRVvyqx8duatiHrs5Kj9OCPNHafHzEbSZTBBz2rhFTatzR19s4bP/HcwM/2vvBuQJVDcLaK/6KBYvvQXBI4rD7RcfOx/mr/g/LL3zYq9r0hJDBKG2GED/hjOGKjERkR4Tg2cJKbKtpgnbOl2iUyYiNmUm4PS8dQaoClY9+Py0dVvQ+8xVIN1dhdR59D1ZzGII2PuT1zzFqf3QnHEffcztd5lyOI2/DctW3fNyryTPsCDtnUC9KhvZumVf3UeqqFNcNgIej91JIqJemQz/U6NGCUiwjHEp25PDbuAJIiYLl3/z/7J13eBzV1YffOzO7q94lS+5Nttx7r3RTTXGBUJNAQkJCCOn5Ukj/EkIaENIIBMhHMWC6MR0bG/feq9xtWc3quztz7/fHWsKy2u5qZa+k+z5PnuCde2bOFs385swpDBt9D/t2v8Lh/PfxeksxDIuEpB70HziH7K4T64YBnY1hWHTrOZ0uOeM5cWxV0H51yRlPt54zmq2RkNLPwf1vh92DfNf2Zxkzoe1+j80KcyFQrb7Rbtw+NjYdn7c0rD3GxmYgpdNk9D3wukne0NsYNOwOjh35lJKibdh2NS5XAl27TyMlLfe8dPbRaDoqWrxrNG2IIQS5yYn8cuIwTnn9fHD4BKVeP5Yh6J4Qx7SuGQgEZhCP/5R08C5/BlkaWu9u35oX8Uy9FSOla+SjXgpkWUH49t5K8NWAJy5yPkUZQghIicEYmoHcHOLwrmQPxoissIY0CUNAt0Rctw7B/58tQd04iMxYPF8diXJUk91tAr8hgcsVR27ePAYO/ly97bUiraUUlGkX/ZGP3/kaJwvWt+hXRtZIpl34hxbXGYaL40dXtriuKU4cXdmmBdQuVzzeJrrl+LyncLuTMUw30gmvziE2NqPB37iUNr36Xs6mdY+Etc/e/a4K6rxR+31nd51Al5xxCAI3I0JY9bZrNJrWo9NmNJo2plaYJ3tcXNWnK7cM7MX83J5M65qBZRhBCfdafGtfCd0BpfCteO50wWsb0Oqc+vbd8z0YlCMDha0xIQgYAa45A1r18QhDYI7Nxn33SERq80NxjMHpeL47AWKtoNtSNibIghFpQhgYhpsLLvsbQ0feTWxcVqPrYuOyGDrybi687O8YpicoYe33hVZ7Uc/WH9rU11BQSpKVPbbJ7SeOrcQwTHr0ujis/ZtmDD36XIJh1J/8axgW/Qbe0OD1YEhNH0RaxqCQbmgMw8I03RimK/D/LTxN1Gg0oaNvhTWac4grzAuZkg7+nUtRFSFGbk/jW/86MZfdF5ZtswgwkjLDt3fHBv7XwRGmAZmxuO8Zhe+v6wN91Js1ANe8PMzRXVr9tEQYIiDMfzENufkk9tLDyKMV4JeIWAtjWAbWzJ4YmXFhpeeES226xeDhX2DIiC9y5OASTp5Yh99fhcsVR2aX0XTrOR2lQuvB3lwufktYVtv+FgcMvol9uxc2uq2y4hhHD39Cbt5cDux7K+R99+xzWZP+u91J9BtwPbt3PB/SPoeMuKtepyCNRhMd6L9IjaY9ICWy6GDY5qqmHFVTgYhLjqBTIEwXrqGXUb3oIbBDf9TvHjYrov5EM8I0MHon4fn+BOy39uGsOwH+s56GCDDy0rEu642RmxqxNKdaQW4MzcAzon6UW0lVVzR8roT7mdQKw649ptG1xzSEMOqKJIUwG7QobA4pbdLSB3EoP7wC6tT0QWHZBYMQBimp/cnIGtFkJ5w9O15gxiWPhFwPYFoxDBp2e7NrRo3/NpUVRzl6eGlQ+xwx5l669ZihC0w1mihEP8/SaNoFClpb8NVGBWMiJgHXkPAe9Xsm3tgZsmbqEKaBSI/FdduQutaS1oU9MS/ogXVNPzw/n4rn66Mx+qW0iWhqTJwLQ0SFQDMMC8OwTqfUWGFFewMpItdjBDHIqTEC3WyclheGiZQ2k2b8Bk9MWqPbjx35lCOHljBl5u9ISRsQ1D4N082Umb8jIbFHk+kttXUK0y76AwOH3NLs04nYuEwmTP05g4bdERW/C41G0xAdeddo2gNCIOJSWmFvIDwJEXOnHkoSM/0L+Ld9EFLXGWvgdMwu/dvGpyimrvg01sKc2v2zVpkKOJ1rfj4i4B0FlyuBnr0vIX/vmyHZJSX3ISt7dBt5FcAwLGJjM7j4iif46J2vUFlxdvG5YvnH32fmJY9y0ax/sXblbzm4f3GTnVpSUnMZO+mHpGUMbbIbzGfHNlBKMXLsfQwdeTf7dr/Cwf3v4K0pRhgWCQld6Tvgerr3nNH6MhaNRtOmCNXmE1w0Gk0kkFWllD14GTihR9CtAVNJuOXPbeBVACUd7D2fUvncd4JKnzG7DyXh838H0/3ZJFmNJgIo6eD1nuKdN24JejiRaXq48PJ/kZo28Jzkd0tpo5TkwL632b3jeUqKttdtE8Kga/fpjJ74PeLju+CtKWXPzhc5engpPl85pukhJaU//fPmkZE1LOyc9MbsdH67JtLU1NSwf/9++vTpQ0xM+PUo54sjR47wve99j0WLFlFVVUX//v154oknGDu26eLz5ojU56HFu0bTjqh84fv4t7wbsl38rQ9j9R2PMNvuwqykg3N4C9Vv/Bbn+M7GF1ke3COuIPbK74JhBvqGazQRRkqbqsoTfPTOV6goP9zsWssVz7QL/0Bml9EtRq8jTa1Yrqw4Rk11EcIwiYvPJiYmtZ6QPltUK6VQSp5zfzWaUImkeFdSceqgxFeucCcKknsaYbXRDZaSkhJGjRrFBRdcwFe+8hUyMzPZvXs3/fr1o1+/xqeht4QW75p2iTqdTyrLCsBXjfDEIZKyQMo2FZYdASUdnGM7qfjn50PKXzey+pN4z3PnJH9VOTbCtLCPbMW3cgFOwV6U7UPEJuIaOB3P2OvBEw9K6Yi7pk2R0sZxvOzZsYA9O19skKLi9iTTp//VDBx8MzGx6TrirNG0AZESq4Xbbfa87cNX9plkdScJ+s9ykzGobf52v//977Ns2TKWLg2uyDsYtHjXtCuUkqjKErwrX8C39pV6LQ9FcjaesdfjHj8H4UnQ0dhmUFLiW/8a1a/9Mqj+6iIxk8QvP4WITzunN0dKOoE8+zNuGGqFvUZzLglMejUpOL6WyvLDKCWJiU0nu9uk091sDF2YqdG0EZEQq4Xbbba94G1y++B5njYR8IMHD+ayyy7j8OHDfPzxx3Tr1o2vfvWr3HXXXWHvU4t3TbvCu+pFqt96sPmIseUm7roHcA29VF9Mm0EpiX/r+1S/8b+oqtIm15k9hhN/44OIuBQtmjWdnkCqSeDJX0Cw6yc/Gk1b01qxqqRi5Z+r60Xcz8aTJBj/jdiIp9DU+nv//fczd+5cVq9ezTe+8Q3+9re/cfvtzbdmbYpIiXd9Rde0KUpJfCtfCAj3lrB9VL34P8RJB9ewy3QEvgmEMHANvhDXoAvwb3kH7+oXcU7sBbsGEZOIa8BU3BNvxMrJ09FujeY0QgiE0H8LGk174tRB2axwB/CWBXLhU3pHVjNIKRk7diy//vWvARg1ahRbtmxplXiPFPpMpmkzlJTIovzAAJ+gjRRVr/yMpD7jIDFDR+CboPbGxjX0Utwjrqi3TSlVl1KjhbtGEzmkdBqk2QT+3qQONmg0bYCvPLjkkGDXhUJOTg6DBw+u99qgQYN46aWXIn6sUNFXdk3bIcD76bOgZMtrz8Sx8a58npiLvgJCXxCbozFxLoQgpLGUGo2mWWq7vRQXbmP3jucoLNiA31+FyxVHZtZocgfNJz1zqG61qNFEGHdicNeyYNeFwpQpU9i5s37ntF27dtGrV6+IHytU9FlG03b4vfg2LQrL1LfuFWIuvDvCDmk0Gk1oSGlTXnaQT5f8kNLiXfW2+bynqKx4k/x9b5KalsekGb8hIbGbFvAaTYRI7mngThIt5rwn94x8Dcs3v/lNJk+ezK9//WvmzZvHqlWr+Mc//sE//vGPiB8rVHTFjqbNsA9uBF91WLaqsgSnYG+EPdJoNJrgkdLmVMke3nvzjgbC/WxKinfw7pu3Ula6v8mJqBqNJjSEEWgH2Rz9ZrnbpN/7uHHjWLhwIc8++yxDhw7lF7/4BX/605+4+eabI36sUNHhAU2boJREVZe1bh+ttNdoNJrWYNs1fPTu1/D7K4Ja7/dV8NG793Dl9a/o6LtGEyEyBlkMnkeDPu+eJEG/NuzzDnDVVVdx1VVXtdn+w0WfXTRtghAGwh3bun144iLkjUaj0YSGlDZ7d76Et6Y4JLua6kL27VpI7qD5WsBrNBEiY5BF+kDznE5YjWZ02oymzTC7DoJweym7YjAzekfUn86Ecvwo6QR6Wzs2SoZYNKzRdHKEMNm7K7yuEnt2vqiFu0YTYYQhSOltkjXMIqW32WmFO+jIu6YNMRIzsAZOw97xcci27qGXQBiReyWd091tBBidaxBL7VRTVVOGb+2ryOJD4NiI+FRcwy7Dyhmo+75rNEFSUryDivLDYdmWlx2gtHg3KWm5EfZKo9FotHjXtCFK2ngm3hieeJ94EyggiBtrJWVgna8a/65PUFWnwHRhZvXF6jmiUwhW5djI0mPUfPAY/m0fgOOvt937yX8wuw7GM/0O3IMvOk9eajTth+qqk62zry4kBS3eNRpN5OnYikZzXhGGhavveFwjr8K/4Y2g7TyTbsLKGRjUWiUdZGE+3mXP4NvyDvhr6m03MvvgGTcH99jrwDARHfBRtnJsnCNbqXzmXlRN04V1ztFtVD33XZwZXyT2oq+eQw81mvaH0cpprIaeUaHRaNqIzpNToDkvKKWIu/YnuM6aAtoU7vHziJn1rSD3LfFvWkT5Xz+Hb/1rDYQ7gDy5n+q3HqTin19A1VSgHCck/6MdJR1k6dEWhfuZeD9+HO+KZ3UevEbTDAlJPVpp3z1Cnmg0Gk19tHjXtCmBaZ8G8Tf8gribHsLqO66RRQbWgKnE3/owcVd9r97o8aZQ0sG/7UOqFj4AQfRUdo7toOKJu8HxhfEuohdhmNS8/9eghXst1e8/1uE+C40mkiQm9SA9c1hYtpldRhOf0DXCHmk0Gk2AjpdDoIk6asW4a8BU3INm4hQfwjmwEeWrQnjisfqMxUjugnJCGGwiHapf+yWopqeuNTA5sZuapU8Qc8GXEUbHeKQtq0rxb/8wdENvJb4Nb+AefW2HrwfQaMJBSpvcvHkUndwcsm1u3jyktHXHGY1G0yboyLvmnFErEs20HrhGXI577HW4hs/CSO5Sb3tLKMfGt+ntsIY4+da+ErJNNKAcG2X7T/+/DyVl4HNY+wqEctNzBr41C7Vw12iawDAseva5jNT0QSHZpWcOo3uvi7Rw12g0bYYW75rzgjBMhOkKKwIuTAvfqgVhHVdVFOHf9kFoUf7zhFIq0KvdsfFve5+aj/5Bzbt/wfvJU8iT+xCmhXvEFbiGXRbW/p3i8NrgaTSdB8HMSx4lOaV/UKtT0gYw4+KH29gnjUbT2dHiXdMucY7vDN/26HYCfSijF+XY4K2k5v2/Uvb7WVQt+CHeJf/Gu/y/1HzwGOWPzqf8H7djH9hA/NxfE3Ph3aEf5Kx2khqNpj6GYeJyJ3DJlU+Smzcflyuh0XUudwIDBt3ExVc8ieWKx+ggaXkaTWdmyZIlXH311XTt2hUhBK+88kq97Q888AB5eXnEx8eTmprKxRdfzMqVK8+Jb/q5nqbdoRwbZPhdY5SvOvzJr+cA5dioymIqnrgbWXSgyXXO4S1ULfgB9qFNxF3xbZS3Eu+yp4M+johLiYC3msZQjoMwAwJOKYUQAqUUKIUwove3p2mIYVgIYTB6wncYMfYb5O99g8KCjfj9VbhccWRmjaJ3/ysxDBdApxoMp9GcS5RUyJ1+VKlCpAiMga42nbJaWVnJiBEj+MIXvsD111/fYPuAAQN45JFH6Nu3L9XV1fzxj3/k0ksvZc+ePWRmZraZX6DFu6YdIkwLLDfY4XVLETEJICVEa3TM9lLx5FeaFe5n4lvxLMITT8wl9+Lf9Qny5P6g7NxDLkJJp8MU70YDSkpwJM6arTifbkAdKwTHgRg3xuB+WFPHIHp3rSfuNW2HUhKlJELU3kg5CGEG1dHqTGoFuWXF0Dd3Nv0Hzjm9P4WSNobpiqzjGo2mHvZqL77/VqKKP2txLNIM3DfHY43ztMkxL7/8ci6//PImt3/uc5+r9+8//OEPPP7442zatImLLmrbYYhavGvaJVbfCdi7loZl68qdDFEa/VTSxrv8GWRhfkh23qVP4Bl7HZ5xc6h+68GWDYTAPfHGqH4C0d5QSiHXbcP/8vtQ462/scaHXLcd37rtiB7ZuL9wHSohTgv4NqK200tF+WH27X6V6qoCQBAX34U+/a8hMalH2N1gaiPsEOikJbRw12jaFHu1F+/D5Q1eV8Uy8PrXaTMBHyw+n49//OMfJCcnM2LEiDY/nhbvmnaHcmw8E+aHJd6NzL5YvUa1gVeRQuBdszB0M+ngXfMyMVNuo/q9R8BX3exy18AZmKndwvRRczZKKZxl67Bffr/ltYeO4/3j03juuwWVmIAw9Q1UJFFKcvLEBrZu/DsFx9c22L5t0+NkZY9j6Kgvk5k1Uqe5aDRRjJIK338rm13j+28l5hh3m6bQNMUbb7zBjTfeSFVVFTk5Obz77rtkZGS0+XH1WUvT7hCmhdV/IkZ6z5BtPRPnR22nGeXY2Ls+QZWfDMvet/YVcMfhGjCt2XVGl1zi5vxCT1iNEMqRqPyj2As/CN6orALfP1+E83Cx6cgoJdm/53U+eucrjQr3WgqOr+bDt+8mf++bKKX/DjSaaEXu9NdLlWkMVSyRO89PA4YLLriADRs2sHz5cmbNmsW8efMoKCho8+Nq8a5pn0iH+M/9ATyNd39oDNfQS/CMmxO9vc2VxAkyX71R84oiVE05RmJ6k2usvBkk3vVvMN26cDJCCNPA/nBlSAPDANSxQuTOfJSjxWMkkNLm2OHlrF7+C5RquaBdKZtVy37O8aMrkUFMadZoNOceVRrceTXYdZEmPj6e/v37M3HiRB5//HEsy+Lxxx9v8+Pqq7emXSJMCyOtB4l3/huRlNXievfoa4mb86tAx49oxva2vKY5lMQ15BKMLrngigHTQiRm4p54E4n3vULC5/4AVkz03sC0Q1RZBXLb3rBsnU/W6bSZCGEYFuvX/CGkSLpSDhvW/EkPVNJoohSREtzTyWDXtTVSSrzeVl7Hg0CfsTTtFmFaGBm9SPrma/g3Lca78nmco9s+W+CKwT38cjwTb8Ts0r+uZV/0IhCxya0wFwhPPGb3oSTd81y9TUpKOP3edcQ9cijbwdm0C2R4N4Vy+z6U7SAsXbjaGqR0KCzYQPmp/JBtT5XsprBgE2kZQ3R/do0myjAGuhBpRrOpMyLNwBgY+cLxiooK9uzZU/fv/fv3s2HDBtLS0khPT+dXv/oV11xzDTk5ORQWFvLoo49y5MgR5s6dG3FfzkaLd027pjaC7Bo+C/eoq5AVxajqU2C5MRIywHLVzWOKbuEOmBauwRdSveghCCMP1+o3EWG5G92mBXsbUtl8cXCzKBXoTJMQFzl/OiGGYbJ350th2+/ZuYCJWcMj6JFGo4kEwhC4b45vtNtMLe6b49ukWHXNmjVccMEFdf++//77Abj99tv529/+xo4dO/jPf/5DYWEh6enpjBs3jqVLlzJkyJCI+3I2WrxrOgS1It5ISEPFpYAQyPKTyKJDIARmek+MxAyUY0dtyogQApGUhZU7GXvXJyHbeybMj+r31zFR0NqoeSdKm6ltz+j3V+GtKUEIgScmFcuKRUqnVZHv8vJDYdtWlB8O21aj0bQt1jgPfJ1z3ud95syZzabavvzyy21y3GDQV3lNh0E5NspXjW/NS/hWv4QsPfrZRiGw+k/CM2E+Vu4UQEVlizjl2Him3Iq9e1lIBZBGei+sAVOi8j11aISByEwL3z4+Fjzntz/xuaBWmBccX8vu7c9x9PAndUWlhmHRreeFDBh0I5ldRp4eqhT671i1YuqyLljVaKIba5wHc4z7nE5YjWa0eNd0CJR0cI5spfK/96GqyxpZoLB3L8fevRwrdzLxNz6IMl1RN11UmBZW7zHEXPw1at59ODibuBTib304MDW2E0VxowFhGhhD+0NiPJQ334u4McwJwwjkdXXcC5CUNra/io/fu5eik5sa3X4o/x0O5b9Dl5wJTLvwIQzTE3IU3hOTGraPrbHVaDTnBmEIzEGNp4Z2NvSVXtPuUY6Nc2wHFf/5auPC/Szs3cupfOYbIbf2O1cIIYiZdgexV30v0DGmGYysfiR8+WmM5C46XeZ8oWpFeIgIgTV1TF0hcUdESQfHruG9tz7fqHA/mxPHVvLB219CKTukrjFS2vTofXHYfvbsfYmOvms0mnaDFu+a9o9hUPX898BfE7SJvX8N3mVPRe3AJgD32Dkkf/ddYmbdj5He67MNhoUrbybxdzxG0tdewEjK0sL9PCJMA+uC8Yj0lJDszOljESmJrSqkVlKibCcwKMp2UE74qSNtgTBMVn/6S8pOBT+/oLhoG+tX/zGk4xiGRe9+V+JyBz/3oRa3J4VefWbpdpEajabdoM9WmnaNcmzsPZ8iS4+FbOtd/RKeaXdE3qkIIQwDPHF4JswnZvLNgZxex484HY2vvfHQwj0KcLtwfWU+/r8+hyo+1eJyc8IwrGtmhn045TgI00QdOIazc3+gY43LhdEzB2NIP1BERf/4mupiDuWHMHn2NPl7Xmfk2G9gWbFB2xjCJDdvPts2hTYgZcCgG0HXimg0mnaEvupr2jXCtPCufD4sW3XqOPbOpVi5U6JaANf6JgwTzsgDjmafOxvCNCE5Afc3b8N+ZxnO6q0BQX32upxMzOljsCYMD2tgWO2sAmfVZpxP1qGOFdbb7gAkJ2BNHIF54QQwjfPWJlRKm907XkCp0J9u2XY1+3a/Sv+Bc4KOiAvDZNior1BWup/DB4O7YejR+xKGjLgr+tvIajQazRnoq7+m3WMfWB++bf46rNzJEfRG01kRpomKi8GafSHWVTNx1m1DHTsJfhtiPZhDczF6d6tLbQlVMCqlQCl8T7+O3LCj6YWnKrAXL8PZuhf33fNQMe7zIuANw+L40U/Dtj9+5NNAVDxEplzwO9av/gN7dixASn/jvplucvPmMXLsfXT0gmGNRtPx0OJd065RUoaU697A3lsJIro6zmjaL0KIQAGqYWCOHVL7YmAC6+mWZsIM7/cmhMD/3KLmhfsZqMPH8f1jAe6vfy6s40UCv68ibFufr+mhLE1R22Jy1LhvMWTEXezd+RL5e9+kqqoAgSA2LpPe/a+i/4AbcLlbV2+g0Wg05wst3jXtGmEYgY4sYQp44YkH5aBrtzWRpp5Ib+XPS0mFOlaAs3pLaHYHj+Gs2ow5fljYNw2twbSa75bUHKHku5+NEAKPJ5m8obcxePgX6m2rHRSl0Wg07RWtWDTtHqtH+GPNzZ4j0I/MNVGPAGfJ2rBMnWXrz4twV0qSmjYgbPuUtAGtGrwENCrStXDXaDTtHS3eNe0a5di4x88Ly1YkZeHKm6ELPzXRj9/GCTJd5mzU0ZPIg8cj7FBw5ObND9NSkDtovu4Co9Fozivl5eXcd9999OrVi9jYWCZPnszq1avPt1tavGvaN8K0cOVNRyRlhWzrGXs9gWK1zo1SEuX4UbYv8P8hDMfRnBvUqfJA4Wu49icKUc65/V6FMEhNzyMtfXDItjndJhEfn61z0jUaTR1KKpxdxdirj+HsKkbJtr9+33nnnbz77rs8/fTTbN68mUsvvZSLL76YI0eOtPmxm0OHHDXtH6WIn/sbKp68G5zGu0ucjdljOJ5pdyA68SN05dgI00IW7MfOX4vyVSHccVh9xmJm9a3brokC/K1LH1G2zfm4UZXSZtyUn/D+W5/HtquDsnF7khkz8fs6N12j0dThrD+Bb8FOKD2jBW+KB/fcgZijurTJMaurq3nppZd49dVXmT59OgAPPPAAr7/+Oo899hi//OUv2+S4waDPjJoOgdljGPG3/IXK574N3spm11p9xhL/uT922kfytb3C/Vvfw7vyOZxDmxusMXuOwDNhPu5hl9Wt15w/RHz4hZ8AIi4WzlO7yOSUvsy45BGWvP+NFrvPeGLSmHnJo8TFZ2vhrtFogNPC/Z+bGm4o9eL75ybcdw1vEwFv2zaO4xATU//8GxsbyyeffBLx44WCPjtq2iV1qR2+Gvw7PkIZJu6B00n6xkK8qxbgW7sQVV5/gI3VewzuCfNwDboQUIGhR52MQK9wSeWCH+Hf8k6T65yDG6k6uBH/jiXE3fBzFIYW8OcRkZyI6JqFOloQurHbhZHX57x9f4ZhkZ45jMtnL2DH1mfYv+c1/Ge1gfTEpNI391oGDr4ZtydJC3eNRgMEUmV8C3Y2u8b34k5iRmQhjMie4xITE5k0aRK/+MUvGDRoEF26dOHZZ5/l008/pX///hE9VqjoM6Sm3aGkgyw6hHfZ0/g2v13XJrImOZuYyTcTM/kWYmbciXNkK7KiCGF5MDJ7Y6Z2C6SCnKeJk9GAEILKlx9oVrifiX/z21SZFvHX/6yNPdM0h3IczCkjsRcE972diTl6MLhdbeBV8BiGRWxcFqPG3seIMV/n6KElVFedBCGIi8+ha/cpgMDoYDfUtpSYQtTdODlSYpzxb41G0zxyT0n9VJnGKPEi95RgDkiL+PGffvppvvCFL9CtWzdM02T06NHcdNNNrF0bXvevSKHFu6ZdoaTEv/0jql76Edi++ttOHad60UNUv/co7uGXY/WfiJU7GWHF1An2zpzDraSDfWA9/o1vhWTn3/AG/tGzsXqOaHdPK5SUgT7+hgmnnzoI8/wK2XAQpok5dij228ugvPm0sHqYBubMsYH3fp4FY2CAlYmJSbeeF9Q9PRPC6ICi3cEUBqtPHuC9ozsp9VbjMgx6JKRyQ++RZMclYUuJ1YkDCRpNMKhTLQj3ENeFSr9+/fj444+prKykrKyMnJwc5s+fT9++fdvkeMHSeZWMpt2hHBt7/xqqFvwQZDOdN/w1+NYuxLd2ISI5m8S7n4bYlE4dcQcQholvxXNh2fpWPIer9+gIe9R21BXjFh/Et/EtVEUxCAMjtSvu0bMx4lNR0mlfNyOGwH3XHHyPPgteX8vrBbjmX47ISI26335ArLejzz5EXsnfxDN71nCkqrTBtv/sWsnErN58fcgM+iZmYEbZd6PRRBMi2RPRdeESHx9PfHw8JSUlLF68mN/97ndteryW0OJd034wDKpe/UXzwv0s1Knj1LzzMLHX/rgNHWsfyMoS/DuXhGXr3/ExsrIEIz41wl5FHqUU9r5V1Cx5HOfAhgbba95/DNegmcRceDdGWg+EaaEc57wMMgoFYZqQk4n73pvxP/4yqvhU04tjPbjmX44xLFenaJxjfr3hHRbmb2xyuwI+LchnfdERHpp4HWPSe2gBr9E0gdE/FVI8zafOpHoC69qAxYsXo5Ri4MCB7Nmzh+985zvk5eXx+c9/vk2OFyz6jKFpFyjHxt65FHUq9GEzvi3vgC+4NnUdGefEbgh3YqW0cQr2RdahNkAphXf5f6l85t5GhTsA0sa/9T3K/34b9v61qJoanE27UH4/SspAUW+UIkwDkZWG+4dfwnXnDRh5fT7rIiNAdO+CNX8Wnp/dgzG0vxbu5xClFH/fvqxZ4X4mNY6fb69YyIGKYhypZytoNI0hDIF77sBm17jnDIx4sWotp06d4p577iEvL4/bbruNqVOnsnjxYlyu85t+qSPvmnaBMC28qxaEZ+yvwbfuVdzj53XqnPdW38D4qiLjRxuhpIN/82JqFv8xOANfFZXPfYuELzyOSEnA+5NHMScMw7pqJsoQUZdqUkvtEwJjYB/Mwf0AUH4bLBMhRLt4itARqbR9PL17VUg21Y6fv+9Yxm/Hz24jrzSa9o85qgvuu4Y37POe6sE9p+36vAPMmzePefPCm+LelnRiJaNpbzjHd4dve2xXoGixEyPiks+rfZujFNWL/xSaja+amvcfIeHWh7EzUnCWrEUeOIb7K/NRlmizaE4kEOZnNxfCZZ3xeuf+nZ8PbOmwMH8j3hBS+mr5+NgeSrxVpHri2sAzjaZjYI7qQsyILOSeEtQpLyI5kCoTzefotiQ6Q0saTSOo0y0hw7Xt7CkEZtfBiNjwBLiIS8HsGvqY+3OFcmz8295HVRSFbGvv+RRZfBRz8sjAvg4cxf/kK532oqAJHcswee1Aw2FnweAoyWsHNuMPN6VNo+kkCENgDkjDGpeDOSCtU5+jtXjXtBtETEKrbFVnzys1TNyjrwnL1D36mvPearA5hGnhW7swPGOl8K57GXNUXt1Lcsd+ZP4R/ZvRBM3RqmYKiFvgSNUpjCj++9JoNNGFFu+adoFSElf/SWHbWwOmgurkQkwYeCbMh1D7nJuugJ2I7tOFU3QobFtZdBgREwPWZykn9tJ1UX3DomkdUilkBIuTW7MvqSQC/VvTaDTBEd1XY42mFgWeSTeFZSqSsnDlTe/cxaoEhuSIpCzirvtp8KJUCOKuewCRmBn9aUeOv/W2Z+SLy007wR96DrMmerGlg1SKjUVHeP/oTj48uoudpSfqtrWGtFbkrKd54nGCCC5IaQc6KtWUUlV5HJ+3vO51jSaaieYuXueSSH0OnVvNaNoNwjAws/ph9h6Nk78uJFvP+LkgJZj6XlUYJq5hlxFnWFQtfACaqyNwxRB3/c9wDb4QEeVRdwjk5YeT815nKyX4zhh+5EhURRUiLcoLdTUtYkuHKtvPi/vXszB/E8ery+pt75eYwfV9RjC713BMYYQ8+dRRkku65/HfPWvC8u+KHoMxm/kbU0ri85WzZ8cC9u56marKz1rmpqTm0j9vLr37XYVhWBiGvqxroofalopVVVXExsaeZ2/OP1VVga5trW01KZS+HdK0E5Rjo6pPUf63W1BlBUHZWAOmEn/zH9uF+DyXKOmArxrv2lfwrX4RWfxZyomR3hP3uBvwjLkOXDHtYgqpkjY1H/0L70f/DMs+/paHMczu+B+tP4HW/T9fwkhPiYCHmvOFLSV7y05y76cvUuxtvt1pr4Q0Hp0yj3RPHFYIv3ulFMery5j9zj8I9YI6Mr0b/5z2uWbX7Nn5IutWPoiUTT9dcrkTmXrB78nKHo0Q0f83q+k8HDt2jNLSUrKysoiLi4v+p7htgFKKqqoqCgoKSElJIScnp1X70+Jd065Qjo2qLKbiP/cgTzY/NMg1bBZx1/8MhBG1PbvPN8qxEaaFrDqF8lUh3HEYccl1r7cnZEUxZb+/PKQJvABGaneSvvkqvqdeQ27Y8dkGIfD86huIGHeEPdWcK2zpcLiylM8v+S8V/mYmNJ5B17hknpp5KwkuT7PR8Mb44erXePfIzqDXC+CPE29gQlavRm8WlJLs2vYs61c/FNT+DMNi+sUPk5U9RkfgNVGDUorjx49TWlp6vl0576SkpJCdnd3qGxgt3jXtDuXYYBjYO5fiXfkC9r6VUPszdsXgHjYLz8QbMbNzUUp1yrv8zkrlSz/Bv/HNkGxir/o+7kGz8P787+B8lndsDO6H+84bIu2i5hxzx8fPsLXkWEg2l/cYzM9GXxHSuUMqhV86fHXZ82wqPhqUzTeGzOTm/mMbPY6SDqUlu1n8+s0QQjzfcsUze+4iXO7wu3NpNG2B4zj4/a2oTWrnuFwuzAjN4dC35pp2R21E2MqdgitvBspbiaw6hTAtRFwKmFbdtU4L986DkpK42T+iougAzuEtQdm4x1yHZ/xc/AsW1xPuAOa00ShH1huGpGk/SKXYU3YyZOEO8P6RnXx72EUkuWOCtjGEwBIGj02Zz282vsOiQ9ubLEJN98Tz9SEzuKLH4KbPUcJg59b/EopwB7D9lezdtZABg2/S0XdNVGGaZsTEa2dHR941Gk2HQUkHHJuqhT/Fv/W9z57InI3lwTP1NmIvvBv7g5XYb3xcb7PokY3nm7edA481bYVUil9tWBz28KSvDZ7Ozf3HhVy8KpXCEIISbxUL9q/ng6O7KPVW4zJMesSncG3vEVzYdQBC0Gxajs9XzivPX4J0fE2uaYqExB5cdcOrIdtpNJr2gb4t12g07RYlVaB/vxAIw0AYJgpB/Lz/RZYew7viOXybF6MqS8AwMFK64h49G/foaxExCfhf/wjnw1X19ikyU3F/aQ5KSl0r0Y4xhGBLkOkrjbG55GjIwr32uACpnji+OGASX8qbUm+7LZ0W96uU5PjRFWEJd4CK8kNUVhwlPqFrWPYajSa60eJdo9G0K2pFtaqoQu45iKrxIdwuRO+uGGnJddF2kZxNzKX3Ejvrm/Xty8txlm3G/nQjlJZ/tkEIjKH9cd14ObhdWrh3AKpb0fu/2m59bq7ZyG8omC42Skn8vvIW1zWHz1umxbtG00HR4l2j0bQr1IGj+JesRW7eHejfX4sAI7cX5tQxmEP7B146QyjVif6SCuTJEkRsTGAoU6wHY2AfrKmjEMmJOuIeIZRSyNP52hV+L37pkGB5iLFcp6PPbZ/7GmeF3ykovhW2rUdgmsHn2zeGabXOXqPRRC9avGs0mkZpTGBFWnQp6QAiaLHsf2spznufNrEzkLsOIHcdwBk9CNdNV6KMz4qWa48hunfBdVP9TiJnCnYt3CPDiepynt+3jtcPbuGUr7ru9XEZPZnbdzTTc/qhlGozES+VZHR6D/aWFYZlPzqjxzm7yTgbwzBJTR8Utr3liic+vnV9pDUaTfSixbtGE2GUUqBUxERgXS/2imJk2QlQEiMhAyO5S8T7sdfWr5f7a3hx/wbeObKDoppKTGGQE5fE1T2HcWXPIbgMAyPIHti1BXx1+1cSlMTev+Z0LrqFmdkbM3tAk+9HKYXzzvKmhfvZx1y3HT8C9y1XNdjW2PeiBftnSKWQp7ukGMKo++5C4c9bPuK/e1Y32idldeFBVhcepEd8Cn+eNIecuOSwcstbRnBjv9Es2L8+ZMsY08XsXsOwDBMp7QZdW6R0MNpY1Cen9CEjawSFBRtDtu3d9woMs3UTHDUaTfSixbtGEwFqRadTfBjn+G6wvYi4FKxeo8B0BQoqQxRBSjogBPbuZQ372QNm96F4JszHNfRSgFaLeOd0CspDmz/glQOb8Eun3vYibyVbSo7x8LaPuXPgJG7uP65xv0+nS0il2Fh0hBJvFR7Tol9SBt3iU/CVF2F/8hS+lc8HhHzt+8nJwz1+Du4RV8Hp4tPa/amiUuzFy0J6P3LdNpzRgzAG9tHtHoPAkYEklxUF+ymorgCgS2wCE7P6AI3nbzfGr9cvZuGBTS2uO3R6eNKTM24hOzYx4hFuQwh6JqQxNqMnawoPhmR7eY/BeEwXjuPjwL5F7N21kIqyg0hl4/Gk0qP3xQwYdCOxcZkoJdtkgrOUNrl588MS7wMGNz+xVdM+aPzGseFrms6HbhWp0bQCJSUI8G/7EN/K57Hz19bbLmIScI28Gs+kmzCSs+vlYDe7X8dB+aqofOZenEPNCyEjvScJt/8VkZgZtoBXKiC2v7niJT4tyA/K5ub+Y7lv6AX1XnOkpNBbyXN71zZIl4BAKsK83iO4oOsA/Ds+pvrFH4Fdf/Kl2XUQ8bc9iohJCHSPkRL7lQ9wPlkX8vsyBvbG/eV5Idt1JmzpUO738uzetbx2YDNF3sp629M98czuNYwb+40h0eVpUmT7pcN7R3bwk7VvhXT8vonpPH/RF8L2vzlsKSn2VnLbR083eF9N0S8pgyem34y34gjvv3VHk4WjQhj07H0p46f+FCGsNonEKyVZ8t69HDuyPGibgUNuZdS4b7a8UBOVBCSZQvqrObFtIcV7P8BfcwrDtIhJ6U320BtI7j4+LBGvpNPgGqRvBtonWrxrNGGipATHR+Wz38be00I6h+ki7vqf4Rp6SYtROiUl2F7K//l55IndQfkiEjNIvPu/iLiUsAS8UooHN70fcorBj0fN4ooegwPpBUqx5PgefrT6DbzSbtZuQmZvHhx3Ncb+1VQ/+y04K8pvdMkl8a4nwBUDjoP3J49ATRht8wR4/ufLiLTk0G07AbaU7Ck7yb3LX6TEV9Xs2jRPHH+ZPJd+ielNCvhbP3yKHadOhOzHY1PmMTK9R5ukz9jS4UR1OfcsW8CRqtJm1w5JzeHPk27A9JWxaOF1OE5Ni/tPyxjKRbP+iWG6Ih6BV0riOD4++eBbHD/acspY/7x5jJ34/Yj6oDl3KOmgUBz45CEKtr+GtBv//cUk96T3tO+Q0nNiUNcTYRj4Kgs5vvkFSvI/xq4pw7DcxKb2pcvQOaT0nIxSjhbx7Qgt3jWaMFHSofKpr2HvW9XyYgBhEDf/d7jypjcbgVdKUv3Gb/GtfjEkf6x+E0i4/a8h2dRS7q9h1qK/4jtLRLdEr4RUXrz4ThwpWXkyn/tXvIwT5CllTEYPHpk0B/9H/8T78b8abPdMvY2Yi7+GKjyF738bbg8W163XYIwcqKftnoUtHY5UnuLzS56h3O9t2QBIcsXw5IxbGuSpSyXZeaqA2z56OixfZubk8uCEa8OyDQZbOkilePvwdhbsW9/gBqO2iHZGTj9qakpYtPA6/L6KoPffo/elTJn5v5F2GwicDwD27X6V3Tuep7R4V4M1XXLGM2DQTXTrOaNNfNC0PUpJlHTY/vrXKDuypmUDYdLvwp+SOfDyJgW8kjbS8bP3g59TtPd9UI2f3z2JXekz/Xuk9JrcJilgmsijb7M0mjBQjo1v9YvBC3cAJala+FOSv/sONPeI3e/Ft/HNkH2y967EKT6Ekdo9JKFqS4eX928MWbgDHKgoYc3JA4xI785P1rwZtHAHWFt4iOf2rWf+xBvxfvIkOPWj9b51rxFz0VfBal06gvL5Ai0lo3wst1LqnN5gWIbJbza+E7RwByjz1/C/G9/l0Sn1U5EcpVhybG/Yviw7sS9s22CofVJwRY/BXNNrGEcqSymsqcQUgi5xSWTGJGBLB0MYLHv//pCEO8Ch/HeprLyPuLguEf8Oa8VUn/5X02/AdRQVbqXo5GZsuxqXK4GcbpNISOyObOFplya6EcJgzwc/CU64AyiHfR/+nNiUHiRkDUacFTVX0kHaPrYs/CJVhQ1v+M7EW36UHW/dT/+LHiAj97Kg0zs15w99i6XRhIEwLbyrFoRu6K3Et+ENlNP4hVY5Nr51r8JZueJB737F8/WKWoPBMkzeO7IzrOMBLDq8HUsYlPlbTjE4mxf3b8AVn4pr0IUNtqmqUvybFyPiPWH7BiBiPRCl3WSUI1FSIvcfQW7ehbN1D/Jk8eltod9MBYtUioMVxawtPBSy7eqTBzhcWcqZD20Fgoowvv9a/NLB14bvt5ZaEd8tPoUR6d0YmtaVzJgEAEwhKCnaQVHh5jD2rNi97bm6KHlbUJvSkJY+iH4DbmDg4Jvpl3st8Qnd6m3XtD+UUtSUHaNw19uh2Umbw2sebyDcITDjYtfi77Yo3D/bmcPeD35G5ckdp1v4aqKZ6LyiaTRRjJIO/vx1yML8sOy9Kxc0mZcuTAt7f5CRl0aw968Oq+1hcZDFfI3a1lQihCDBFbrIPlJVysrj+zDHXtfodvvwFrAsSE4IzznTxOjfM+pSZpQjURVVOO99ivcXf8P38H/xP/kq/sdfxvebf+H9yzPIjTvrxH1b8MK+0FsoAijghX3rUGc0glQoPK3odiQAyzAafa9KqTb7DOofR3JgX2ji6UwO5r/b5u0jIRChNU0Xpuk+nWcfXb9tTRgoyfHNz0GjzVWbp/TgcrwVJ+rdTCvpUHlyJ6UHg2ut+5mdzZF1T+rIeztA36prNKEiHZyDG8I3L9iDsn2IJiY4qhYKB5slTBEebBvAxqiNZjphRh03lBxjZE7fRrcpXzUYJtbEESG3igQwRgyA2OiaNKmkROYfwf/vl6G68ZQVlX8Uf/5RxCfrcN81B+VxR7QXvSEE20tDLyytZUfpiXp9/gWQm5wV9v76JmVgCIGzZTf24uWogiJwJMTFYA7LxZw2FpGT0abTb4UwqakpDtve2wpbTXRR25XF9pYHhLHjxxWbiicxG+nYGBGcrQGBKHnhrkXhOsvJHW/QddRtiNre/kJwfPPzYe2uJH8Jvqpi3HFp4fmjOSdo8a7RhIoQAVHZCpS/pknxLlyx4e/YHReWWde4ZI5VlYVlmxOXhNexqbL9YdlXOz6MJvwWnniQDuaUUdjvrwA7tMe51rSxgTSiKIlOKsdBHSnA/48F4G85R1nlH8X32PO4v34zKoxZAc1R44T3fQFUn/VdW4bJRV0H8Ht3LKVh/G3c0Hsk/tIynCdfrZ/2VVmNs2ITzopNGLk9cd0+O3Aj0yb1C6pVkfPGUhc07Yvanv3lxzdybNPzlOz/CHVGLUF8Zh5dhs4lc+AVgBExEa+UxF8V/s2fv6qwrjaidoBY4e7F4fkibYp2v02XoXP1oK8oRqfNaDSholRAVLYC4W5coCvHxuw+JOz9Wt2GEGoDKVs6zO41LOxjXt97JB8cDTKvshHiLQ+yiScGVt/xgf+Ii8F10xWBEG+QWLOmInpmR9f0VMPA//TrQQn3WtThE4GnDhFuDBZOmlNztgLBNWH8juIsF1f3GALLNjT7HuXug/j+9Ax4fW2SRqOUQ3xC97DtExK7RdAbzbkm0O3FZsdb97N14V0U732vnnAHqDy5g30f/oL1z1xLzamDDbafPwS1KTeGYeL4KptsMxkMvqqiCPmlaSui6Kqm0bQTDAurb+PTRYPB7D70s8ebZyFMC/fYGwJTWcPAPfHGsApWL+mWR0oTNxTNMTq9O70T09hYdDhk21rGpnVFNVI/IJKyAm01TQthGBgj83Ddek3L3WcEWFdOx7p0clTlAytHInfmo4pKQ7Z1Vm6KqHi3pWR8Zq+w7Sdk9cI+S0AbQvCFARPplRDa4/ZvDb0QFyLwHltAFZbge+KVNrkhMwwX/QZchxDhRVP75l6rO760Z5RixxvfoGT/xy0u9VWcYOvLX8Rbfiwi37kQBu6ELmHbuxO61A/atLLdo24XGf3ob0ijCRFhGFjdhmDm5IVl7xk/t8luMwAiNgnX4ItC3q/ZbQhWzsCwhI0Qgq8MmhqSjcswuWfwdPzSYXRGz5CPCdAnMZ1RWb1x1rzcYJtn3A2BFo9n+GgMH4Dnp1/FmjUVUhLrG8TFYM4Yh+d/vox54YSw/GlLhGmENSUWgMpq5IadEetAYxkGc/qMxArjIu02TK7vPbLBQCUhBB7TxWNT59MnMb3F/RgI7h96AVf3Gobz3zehIrhaD7X3EPLIiTaJvsfEptGtx/SQ7Swrlr65s3XHl3ZKoFDzCU4dDr71r+0tY+ei70TkO1fSJjPvqrBshWGRNejqeikupisWKyYlbH88iTlh22rODVq8azRhoBwb94R5LS88CxGXgmvYZc1PQVWS2Cu+jUjODn7HngTirv9ZszcFzWEKg+v7jOSuvMlBrXcZJr8YcyVD0nJwGSYXdh1AehipRHP6jMRXdhL/ziX1Xrf6jsMz7fMNPidhGIj4WMyLJuL50d24f/gl3PffjvsHd+F54B6sq2dAalJURdzPROYfaZ1tBN9XsjuWS7qFfgN6Sbc8EptIubEMg1R3LE/OuIWvDJpKl9jEBmtMYXBB1wH8c/pNzO8zCvu5RchNoaVdOUvWtkkdg5QOw0Z/FcsKrXZk8Ig7Mc3WtTTVnE8EJ7a8FLJVVdFuyo5tbHVrRWFYZA+bByL0movUPjNwxX72tEsphbS9ZA26OixfDCuGjAGzdL57lKPFu0YTBsK08IyejWvIJcEbmRZxN/6OlhK3hWEiYhJJ/OK/MNJ6tOxLfCoJdzyGkdaj+ZuCIPhS3hR+PfZq+idlNrlmbEZP/j71RmZ2zcU8I3L72/GzcYdQ8Dctux9zeo/EWf40nHHxswZOI/7mP9Pc5yRMA2EIjIwUjO5dMDJTEZYZEPcRFnW1F+baCYiB12TItQUAeH3h++H1RTRdxJGS7424mL5BRMlr6ZeYwXdHXIxCNdnC0TJM4iw3t+VO4LVLv8yfJ93AfUNn8tVB0/jBiEt5a9bd/G78bAarOPyPPY+zekvovm/a1SY3aIZhkpjUk2kX/TFoAT9w8M0MHvZ5nWrQTpHSpjh/Cb7KgrDsj296LiKtFd1x6XQZcn1INoYVQ/dxd9W/eVAS21dBlyFzCKlI6DQZAy7HsKKrQ5emIUKFdQXSaDRKKVAOVQt/hn/jW80v9sQTf+PvsfqMCfpErxwbHD++tQvxrnoRWXSg3naRlIVn7PW4x89DeOJbLdxrsaWDZZhsLj7KokPbKPIGJlFmxyZzXe/h9EhIrVtzJo6SrC88zLdXLqTSbl6kXtR1AL8YcwVyy3tUv/QjMExcA6fjnngTrj5j2rQlYCjIimK8q17At/51VNmJQLFyXAquYbPwTLoRM61HSL7W/OgvUBVeIZk5ZRTWdRdF9HOxpUOl7eO+T19iS8mxZtcOS+3KnyZeT2xZNeqtpYEnIOOHYfTMQTlOkx1gHCmRKtAVXojAUxvlSOzXPsBZGmYaEeB58Ftt1HUmIOjKTx1g07pHOHr4E1QjY+WTU/qRN/R2+vQPL91BEx0o6bB/6YOc2BLG0D3Aiklh3Bffj5gvuxZ/j+J9H7a4VphuBsz6Hak9Jzd5Tdn70a8o2NowJbEpTE8iw+f9H57EbH0zGuVo8a7RtILakfb2oU14VzyHf9v7cEbqipGSg3vsDbjH3YBwx4UlsJVjB4Y3Hd6CLDkCUiKSMrF6jQqIyQj3HK7FkTKQZ346wnn2vxvDlpIq28fL+Rt4OX9jvfaTpjCYntOf+X1HMyajB05NRaBQ1bAwUrtixCbV9Vc+n9ROyaxe9Ad8q16o91TgbFyDLyLuhl+AaQXlt++JhcjNu8Pyy3XbNRjDciMuWB0pMYTg04L9vLBvPZ+e2I+s7VyBYEp2X+b1HsmELn2wdx/AefIVqPns5kx0zcK6bDLG0Nygo+HKcbDfXILz0erwnBbg+f132jQ9Skobw7CorjrJ3l0LqSg7hFR+PJ5UevadRWbWiLo1mvbN7nd/TOGuFgIwTSAMFxO/siIifgTOPYLDa/7J8U3PY9eUNrouIXs4vafcT0LW4EbPO1La+CoK8CRksevt71O8v+WbAdOdQN5Vfyahy1D9m24HaPGu0USAWtEpa8qRRYdQthcjNhkjsw9IJ2ICO5CmoM67wG0JWzqYwmBveSGl3mpiTIvuCamkuGNxpGzVUKi2RilF1Qs/wL/13aDWm92HkvCFf4JhNRkVV1KCArn3EP6/hTE8JTEez0+/0qZPI2wpsQyDcn8NxTVVCAGp7jgS3TH49x1CLduA3LizXhHxmVhXz8ScOS4oQa0cB2fVZuwF74Tlq8hIwfPDL4VlGw5ninQlHRSck2mqmrZHSYf9S37Lia2h57wDWJ5kxt35QcR9UkpRtPddive+j7+6FMN0E5PSk+yhc4lL79fisCilJDWnDhOT3J0j657k+Kbn8VcVNlwoDFJ6TqHX5HuJTemp5xW0E7R412g0mtMo6eBd9jQ17z4ckp171DXEXffTxvfpOFDjw161GdcF4/H+779QBaENZDEvnRxofXmObnqUlKiiUzirNiG37UMdOxmUneuWqzBG5CHMlv1UPj/enzwCvtCHRVlXzcCcMbbN0mY0nQfp+Dm58032ffiLsOyTe0xi8DWPRNirAGc/2VEqEAQI9jyglMRbdgxXbCrCdFO87wNK8pdg15zCMD3EpvUma/D1xCR1bZPJsZq2Q39TGo1GcwbeFc+GbOPbtIiYy+7DiEuu97pyJFRW4/vLf1Gl5ZjDcnHdcjW+R58NunhV9O6GdfGkc1oDIAwD379fRp0IbViL/dZS3KMGBbfYZWGOGYzz6cbQnLNMzEkjtHDXRATDdJE58AoOLPsjjq8iZPvsYfPrRHZt4agwzNMF3Xaruracnb4ihBFSDWpt/3jDtPBVFZGYPYKM3EvrtkvHDjwx1sK93RG9z641Go3mHKIcG//2j1DljTxabgnHj2/1i4226vT9/QVU8SmQEv+/XkKkJeG+ex7EtzwUy8jtifvLc8E4d60vlZQ4+w6FLNwBVPEp5I79gZuWFheDdelkSAytxah1ySSI0W0ZNZFDCJPMvNBbK7oTs0ntPYVaRV1+YjP5y/7I7nd/xN4PfsbRDU/jry4BaHU7yXCpFeWu2FTc8Rl1rwdSPQPdubRwb3/otBmNRqM5TeWC/8G/+e2wbM2ug0i8+5m6fyvHQW7cif+ZN+qtEzmZuO+aA24LZ9UWnOUbUIUlZywAY0BvzKmjMQb3CxQln+MaAd9/30Cu3RaWrTEsF/fnrwtqrXIcVEExvseeD2pIkzl9LK5rLwzLL42mKZRSSH8Vm1+8neqS/UHZCMPF4Nl/JTF7OCd3Lebo+v9QXby3kXUWqX1m0nPiPcQkddU55ZqIoH9FGo1GcxpVfSp826rSev8Wpon9yfqG646dxPuH/2BNHY05cTjWzHHIwydQlVVgmhhpyYi0ZJQT6O7TFsOIWkIVl7W8KAK2wjQhKw3P/bdjL/4EZ9128Dd8eiG6d8GaOQ5z9OCw/dJomkIIgbA8DLn2H2x7/R6qCpsfGma4Yhlw2W9JzB7B0Y3PcnD5H5tcq6RN8d73OHVoBYOufvh0hxgtvTStQ/+CNBqNppbWPD423fX+qUrLUE1NVK2own77E+x3l2MMG4DRpxsiLgZlOzgHj+Fs2IHrhksQvbqG709rUEGkvUTIVpgmKikBa94srNkX4qzegjpZDLYDcbGYIwZi9MgOFP62ktqOOvVfazizQNP5MAwLEZPMsBuepGD7axzfsqBBJN3yJJGZdzU5I27CHZ9J4e7FzQr3M3F8FWx/416Gz30aT2LXqO8YpolutHjXaDQaArneZkZv7J1Lw7I3MnrW319pEMVvjkRu2IHcsKOhPyVlqJ45bdrLvClEYjzh5lOKhNBy2AFEbU5/jAdz0sjA0wYhAjcCp99/awpUawX6hqJDvJy/iaNVp3CkJM0TxyXd8risxyAMIepNDNZ0PoRhIgyTrMHXkT1sLhUF26guyUdJG1dsGsk9xiOECcLA8Vex76NfhbR/x1vOoVV/p//F4XW20Whq0eJdo9FoAITAPW4O3mVPh2XuGTenbqBWe0Y5DubowWEPkzJGD2p24mpLCOtMu9ZHJ6VSfHJiH49s/ZgDFSUNti8v2M+ftnzIjf3GcGfeZKRSzQ4i03R8ags4E7IGE58xEIVCCLPuRlpKm4JtC5F26NOSi/a+T5/p38XyJEXUZ03nQocZNBqNhkDeq5nWHavvuJBtjZQcrNwp9YS7SElonT+pSecn6m6aGMNyISkM/+NiMEcPjpo2jlIpXt6/ge+ufKVR4V7LKX8Nf9+xjP9Z/Xoonfg0nQBhmIGUmjP+Fg3DomDbwrD2pxwfBdtfQzqhzzfQaGrR4l2j0dRN9Kv3WiNtDzs6yrGJnXU/uGKCNxIGMZd/G85qBSdSkhC9u4Xlh8hIxThf+e6nsWaGfhNjTR97TttaNoctHVYW5PPgpveDTgF658gO/rptKVI3YdO0QM2pw62yFTpFS9MK2vfzXY1G0ypq0zycozvwrVqAc3IfyvZixCZj5U3HM+Y6cAf6kXeGi40wLYysfsR/7o9UPvst8LXQvtAwib36h7jypjf4fJTjYE0dhb+potVmMCePbFXqSWsRhoE1cxyqoBhnRXBDlIzRgwN926MEyzD5547lyBCz95/dt5Y7Bkwg3qV7yWuaRrWiqFtJ+7x0kWpPBD4jg8qC7fiqi/Ek5hCT1A3TVX8+RmcdMNX53rFGowEC0XbnyFaq3/gtzvGd9bZJwM5fS837j+EeeTWxl9+POl3M1dERhonVZwyJX/k/aj74G/5t78PZj7iFgdV/EjHTv4DZY3ijNzbCNDFG5CHeX4E6FsLgp9QkzInnf4KoUgrXvMsQqYnYH66CmiYmwnrcmNPH4Lp8Gkqp85LqczZSKfaVF7K55GjItl7H5pUDm5nfd5TuQtOOCEw09RNIKAjcsLVmumlLuGJS8FcXh2cbmxoYkmR2/IBIqEhp4/gqObFlASe2LiShyxD6zfwRpjuO4v0fU7D9VbxlR1DSwRWbRnr/S8gaPBvDiukUAaZa9JAmjaYToqSDvWsZlc9/t6EwbQSz5wgSbn8MTNc5HxjU1pyZIlFpe0FBnMuNgUAqhfBX41v7CurUcZSUGInpuEdcgZGc3WKBqnIkVFbhe/j/UEWlLTuTGI/7npsQ6cnnXbzXoqQCx8ZZvRVnzRZUaTkAIikBc+wQzPHDwGVFhWivxVGSv2z5mP/buyYs+wHJWfz3gtsj7JWmLZDSxjAsvOXHKNy1GH91McIw8SR1I3PgFRhWDCgiet5SSpK/9EGOb34hLPsRNy0gNqV3hzuXthYlHcpPbGbHm/fheMvJGnI9fWf8gMKdb3FwxaP4KgsatTNcsXQZModek74OiE7xuWrxrtF0MpR0kIX5lP/9NvAH3y3BNfhC4m98sA09O7fUdhXJLy/i2b1refvwNqrswI2Mx7C4pPtA5vcdQ15KFxzpYNSeKpUCM3ixqhwHvD78C94JdHCRjTxuF2Dk9cU191JIjI8a4X4mjaXxnM/Unpb44erXePfIzpYXNkKKO5Z3r/hahD3SRBqlJGVH13N03ROUHlwBZ6VIGa5YMnJn0X3snbjiMzAiNBxJKUnNqcNs+G9wk4TPJCF7OMNueCIifnQklLSpLNzN1oV3Iu0aUnpNI++Khzi64RkOfvqXoPZRa4MwoiqY0BbotBmNprMhDGo+/ndIwh3Av+0DnMJ8jLSe7T6y4SiJoxQPrHmzUYHnlTZvHNzKGwe3MimrN78dPxu3YWGG8b6FaaJiPLhvn40qr8Reth619xCqxgceF0avrlhTRyNSkwJTVaP0UXpjIj1ahXtrEbrnTNSjlOL45gXkf/L7JgeDSX81BdsWUrzvQwZd/QjxGbkRmW4qhEFsSk9Se0+nJH9JSLbdRt/RafO0m0WY7Fr8/dPtNwW9p95PSf6SoIU7QOmBpeQv+wO9p3677fyMEqLzKqHRaNoMVVMWyOMOA++nz0bYm3OPUgql4BvLXwwqMvtpQT5fWvocfuUggyxSU2dH109H7UViPNYlk3HfcxOeb92O52ufw7pyOqQkBrZHqXBvbzhKkhmTGLZ9Zmzr2nxq2hYlHQp3v03+0t8FNdHXrill+2v34C0/gZKtn9QLgeh77qW/Ii5jQNA2PSZ8hdTe07VwPwslbU4dWoG3LNDBJ7nHBGJTenJ4zeMh7+vElpdwfOWRdjHq0FcKjaYToWw//k1vB5Xn3hi+jW+1+6i7QnGgoph7Bk/j6Zm38dcp87hz4CQyYpqeDLrj1Al+t/G9ZiOyygmICFVWgb14Gb5/LMD76LP4/vUSzrL1KK8v0I7zrMe5wuj4j3jPNQK4utfQsO2v7DEEO0IiT9MWKPI/eSgkC9t7ioMrHo5Y0b0QBobpZuj1j5PW9wJo5txgehLpM+OHdB97p/5bbwRhWBzf/Hzdv7OHzqXixBYqT24PeV9K+jmx5eVAt5oOjL7902g6EwJk2cnw7X1VKF81wh3b8tooxJYSQwgKqss5XFmKoyQp7lhu7j+OLw6cxIfHdvP4zk/ZW9awO8zbh7dz39CZJDXy3pXjQHkVvpffRW7bC/Kz3FsFyG17sd9aijlmCNbsC1CmqaPsbYghDPonZTIstWvIHWdiTBfX9h6uO81EKdKxKdr7HnZ100O3mqJ434f4q0txxaZExBdhWBjCYODlv6em7BjHNz8X8K3mFIbhIialJ12GXE/GgMsRQv+emqP82GctaZN7TOTgikfC3lfhrkV0G3NHBLyKXrR412g6G60N/DQSOarrF398N7KsAAwDI7UbZnrPFjuynAts6eB1HJ7ft5aF+Zs4Xl1Wb3uM6WJW90Hc1G8M/55+Mz9Y9RrLC/bXW+OXDi/t38ituePqCTvlOKiiUnyPPgfllU074fPjfLoBeeAo7ntuQnk6XueeaMKWDl8aNIVvLH8xpF7vN/YbTUwbthjUtA7DtCjY+lJYtkraFGxbSM7IWyOWulLbntCTmE2vSffSe8o3622Xjr9NW1Z2FBx/YKaGMN2Yrhj8VSG01z0LXyts2wtavGs0nQwjqUv4xp4ExBnTR5Vjg+PHt3Yh3tUvIQvz6y03uw3BM34eruGXAeK8iHhbOpysqeCry17gcGVpo2tqHD+vHNjEW4e28rMxV/LghGu5Z/kLbCiqP2DpkxN7+fzAifWNfTa+v73QvHA/A3W0AN+/XsLz9c+F83Y0QWIZJuMze/G9EZfw243vBiXgZ3UfxFcHTdOpDVFOdUl+q2zbYl6FEAIaia5r4R4cpisO21v2WQ1DK3q2d4Z+7x3/HWo0mjqE6cI9/HKwwpse6R55VV0xpnJsZNFByv5yPdWLHmog3AGcI1upWvhTyv92K6r6VEDsn0OkklTaPu7+5PkmhfuZ+KTDj9a8weqTB/nt+Nl4zrrZKPPV79CjHInzyTooDa1ASu0/jLNzf12efKSpy7+XElVdg/L7673eWTCE4Nrew/n9hGvpk5je5LoUdyxfHTSNX4y96hx6pwkX6TQxMCwoW6++OYtCEruOAgJPR2xfBZ6E7LD35U4M37a9oCPvGk1nwxOPa8jF+De+GbrppBtBnBbuZQVU/PsuVFVpi3byxG4qHr+LxC8/hRKx53BSq+DRrUs4WnUqaAtHSX69YTGvXvplLu2Wx+sHt9Rtc5/95EAI7BUbCQdn6TrMgX3Csm2K2r7rcs8BnKXrkNv31XW6ISURa+IIzCmjIC6m0wgYQwgmdenDtJz+rC88xML8TRypOoUtHdI8cVzaPY9LuuXVFSN3ls+lPWPFJOP4KsKz9SQHppvqmoaoQUmb7GHzKNn/MQAl+z8mM+8qjq7/T1j7y8q7um54V0el474zjUbTONIhZvoX8G//AHzVQZu5hs3CTOsR+IdpUPXyT4IS7nWHLTpA9aLfE3vtT0N0OHy8jp+3D4fesaCgpoKlx/cwp8/IeuK9X2IGSimEECilkHsPQklZM3tqGrl9H6qqBhEX0/LiIFCOA2UVeP/xIupEUcMFpeXYb3+C/e5yrEunYF0yKSLHbQ/U1igMT+vGqIwe9bbZ0tHFqe0IJR3S+szg2Mb/C8s+rd+FEfZI01qEYZHSYyIxyT2oOXWI45sXMGzOkyR1HUPZ0bUh7ctwxZGZd3WHFu6g02Y0mlZxdhqIkpJoHlqsHIkqq8RI6EL8/AfBFZxwtPqMI+76B1BKoqTEObkP58D6kI/v2/wOeIPLDW8tfunw+sEtVIfZFvOl/RsZnJpD3zPSLeb2HfVZ7rRUqOLgI/oNUApVFl70sMGuaoX7H59uXLifiSOxFy3F//pHETl2e6KxIVtauLczhEH28BsJp/I+JrkHKT0m6Kh7FCKlzYDLfothxVBxYjOVJ3fQY/yXQx6q1W3U7RhhpoW2J7R412jCQDk2SknsPZ9S+fJPqXjmG1T+37eoefcvyJLDdWuiDWEa2C+/j+9fC7F6jCLhjr9j9hjetIEnAc/kW4i/7ZHTI6cDpwzvp8+F54Dtxbt24Tn5bExhsOtUQdj2tbZdYgPDfnKTMhmW1hWzrhgqem7ShGni+9fLUFEVtI3z4SqczbsCwl+jaScIIYhJ6kZq72kh2+aMuBnZwft/t1cMwyIuvR+DZ/8NKyaFfR//hoQuQ+h30QNBC/guQ26g+7g7O0XBasd+rqDRRJjalAnf6hfxLn8GWXqswRrvsqex+owj5rJvYGYPiKoojzpVHuhDrhS+h5/FdcuVJN71BM6x3XjXvIg8uR9lexGxybgGTsc94kowXdhL1mF064LRr0fgBuDghrB9cA5uREy5NXJvqgkMIahpxU1CbcQ+xnLjMky+O+Li+ikWhoFIat0kTpEQ1yp7CDxNkfsOoY6F3r/f/mgNnmHBT4jUaKIBJR1yL/klm1+6g+rifUHZZOZdTfawuW3smaY1CMMiPmsQo255hRNbX2L/kgfpM/27uOPSObjiUSpObGnUzp2YTdeRt5Iz/MZz7PH5Q4t3jSYEhBBUvfJzfOtebXadvX81FY/fSfyNv8PqNzEqBLySEnvZhroCRnXsJL4Hn0T064E5eSSxV3wXYX7mpywuwX53Jc7KzVBZjRqWizmgV8C2FakvrbENBUdJElrx+DTRFbD1OTa/Gz/7rKh74LdgDOgNifFBt4k8EyO3Z0TEuzANnKWh5YXWovYfRp4oQmSmIQxdqKlpHwjDRFgehl7/BLve/g6nDq9qZq1FzojP0WvyN+qCL5roxTAsDE8iOSNvQQiTmtJDxGcOZtic/1BZuIsTW1/GW3YEJW1csWmk584itfeUz1pMdhK0eNdogkQpRc0Hf2tRuNfhr6Hyue+SeNeTGJl9zvugImEYqOMNo7Nq7yHsvYewXRbExyJcLlR1DVRW1csMUcc/y6UWnviwk0aEJz5My9BQSjG5Sx9eyt8Qlv3ErN44SnL/sAvoFpdST7ifiTlhGM57K0Levzl1dF13mNYitwUXfWzUdvMuzJnjIQpuMDWaYDEMC+GKZfDsx6gs3MmxTc9RtOddpD9QhO9O6ELWoNlkD5uLKzYNaJ+dhJSSKCURwmyX/odLbcFpbGrPutfi0vvTZ/r36n0OUtqBNJlOkCpzJlq8azTB4q3Eu+yp0Gz8NdR88DfiP/dQ2/gUKv5m0kj8NpSWNynK63qFS4nVcyS+gr1huWD2HHFOpq5ahsnU7H50iU3kRHVofdgBbuw3BgF0j0/BaOLCIEwDa9oYnE83QmXwnXtETibGkP4RmbCqvD6Q4UedVFVN66fuajTngdonmnHpufS74Mf0v/CnSNsXiMwbJkraIRc8RgO1fvsqT1J+bCOOvxrTHU9S11G4YlM7bavLxnLZO3pXmabonO+6k6EfFbYe5dh417wEdujDQfy7liLLCzESM9rAsxCJDb8toTjD1jPpRnxrwhhRbnnwjLnunD2FkEoxt88oHtm2JCS74WldGZCcFdziuBjcd94QmLLqbfn3IdKScX95XuTqXa1WXsQtK5pqbzWaQMcu5QAiKJF6pqgzLPdnr7dTYXfqyBqOb3qOkgPL6qWDCMMitc9McobfSNLpoUaazknnes7QSbBPV9Mfrz7J8oJ1fHR8JRuLt2NLB0fqzhLhIEwL37rXwjOWDr51r6LCbFkYKZTjYA7pH7a9MbgfSkqEYWBm9sXsFfrFwz3sUvC0Ps87WCzD4Nbc8Uzt0jdom3RPPP877hrsIKPZwjQR3bNxf+MWRK+cZhYKjCH9cX/zNoiPQZiROf0K00Skp4Rtb2Sn68i7pg5HSvzSwS8d5Dlse1ubHuL4qynZ/zEnd7xB4Z53qCraA4CMwu5dkUYpyd4Pf8n21+6hJH9pgzxuJW2K977H1oV3cmD5n86Pk5qooH3elmoaRZ7+Q196Yg3P57/J6sJN9bane1KY3fMS5ve+khR3ku5vHCKNdZYJyVac389bmCbGyIHw6gchtRQMGAusqaPr0jyUYxN3/c+p+PutQQ9qMtJ7EXv5twMFs+dILCrHAUPw4IRreWDdIha3MLCpV0IaD0+eS6onDiuElBZhGpCZiucbtyKPFuB8sg556Dj4/BDjwRjQC2vKaERKIsqRERPucPqmbPJI7Nc/Ct04PhZjxMCI5N1r2i+1It3r2Cw5vocSbxWWMOiRkMq4zF5IpUL6ewgVJR1qTh3m2Mb/cnLXIqS//vkpPmsw2UPnkTnwcoKNxrdH9n38Gwq2LQxq7dH1T4Ow6DXpa23slSYaESqaJ8pogsZREr/08+3Vv+HTk80Pz4k1Y/jtmO8yMWtUk0V4moaU/mwChBn9cY+eTezsH533/rNKSux3luO8szwkO2P4ANx3XFt/X46NLD5ExX/uQZWdaNbezB5A/G2PImKTzmnhrr1yE86StRhTR+GaNJLdxcd57sAGFh/ejveM73JcRk/m9h3N9Jx+KKVadWOrZODm5MxUNSUlCNFm6Wuq2ov3gUebr2loBPPC8VhXTI9I7r2mfeJIydGqUzyzZzWLDm1rMNQsOzaJ63uPYF7f0XhMM+JBH6UkhbveZu8HP0O10IM9MWcUg676M4YV06EEvJIO5cc2svWVu0K2HXHj88Sm9g3qb1hKG8Ow8FeX4Ks8CUrhis/AHZeOdGyM89xUQRM8Wrx3EGzpcM+Kn7KmaHNQ6y1h8ejEBxiZNlhH4IPk1ENXok4dD8vWM/3zxFxw93nvOAMBIen/10vIHfuDWi+6pOO+71ZwWQ0uEMqxwfHjW/cq3lULkIX59bab3YfiGT8P17BLAeOcRnj9L76Ds3xD3b+N/j0RU0ZhDsvFlg7F1RU4UpIcE0eCJ7Z+D/d2hlIKZ8ka7Fc/DNpGZKTi/uatEOPpsDUxtpQIApNVpVI4SuJqp99xW+AoySfH9/E/q1/H24Jw7h6fwl+nzCMzJiFifydKOhTv+5Bdi79PsIUXCdnDGXLtPzBMV0R8iBZ2LvoOxfs+CNkua/B19J35w2YDQ0o6IAQl+cs4seV5Sg+u4MzPO6nraLKHzSOt74UoVKctAm1PaPHeAbClzTP7XuXh7aF1QklzJ7PokifarWA5lyjpUPPeo3g/+U9Y9olffxEjvVdURDiVlKAU/ucWIddua3at6NcD9xeuB7fVrPCu7R7jnNiDLCsAw8RI6YqZ3uOcdJap54tS2G9/gvPup40vSE7AHJob6LFumaiqGuSxk7jnzYKkhHbd79xevAx78bIW14nMVNx3z4ek+A6ZMmNLiSkEKwryWXp8L+X+GjymRf+kTK7pNYwY0wWoJrsIdQZsKVlfdIivL38RJ8ge2TlxSTw14zaS3J6IfHbSrmHNE5fh+CpCsus58et0HXVLuy1IPRt/dQlrn5zV4pOHxjCsGMZ+4T1MV2yj26W0kb4qtr9xLxUnmg/uxab1Y/A1j2LFpmoBH+Vo8d4BUEpxzftf4mh16KPgfz36W1yUMxlL/6E2i1IKVVZA2R+uCnkYhNlrJIlffLyNPAuP2g5EsqAYZ+lanDVbP+uUYpoYIwZgTR2D0btrXZFqe0FVVgdSSJwQv6dJI7HmXNKuo9BKKeTO/TgfrUbuPtAwmJkQhzlxONYFE1q8IWuPSKUwhOD/9qzhhX3rOFJ1qsGaGNPFpd3zuGfQNJLcMZ02eKGU4pp3/sHx6rKQ7Gb3GsYPR16G0cq/E+nYFGx/lf0f/zpkW09iV0bd+lq7/ls9k5IDy9jxxr1h2w+b+zQJWYMbvK6kRDo1bH7xDqqLg2vt60nMYdjcZ7A8iR0qNamjoRVbO8eWDqsKN4Yl3AFeyH+Ly7pNj7BXHQ8hBCK5C67hs/BvfCsUQ2Km33nOo88tUXvRExmpWNddjHXdRQHxLhXEeMAQdZNY25VwdyTO8vUhC3cAZ+1WrNkXgLv9Po4XQmDk9sLM64ssKkVu2oWqqkG4LESXdIyhuYF1TRTMNpY6FChmbB9RaoXi+6te4/2ju5pcU+P4ee3AZlYW5PPYlPnkxHW+4n1bSlYU7A9ZuAMsPryD+4ddSNwZLRnDwTAtTmxZEJatt/wopQeXk9xjQruPEAc67ITYQOAsHF8TE56FIH/p74MW7gDe8mPs/fDn5F3xh1b5pGlb2vevXoNCsr64+dSH5thUvDOC3nRslJLEzf4xFaXHcA40XxRcS+ys+7H6TzjvhapN8VmKiGjYA74dRrWEaeCs2NTywsbw+XHWbsUcNwzR2t7p55HaaLpIS8acNpq61j6iadEulcLr+Hn1wGZeObCJgxUl2NIh2R3LBTm53NhvDH2TMnCkxIzim7nfbnyvWeF+Jieqy/nqshf47wW3keRuPOWgo2IZBgv2BXcOO5vam58b+oxsVf2Akg5VRbvDtq84sYXk7uPCto8WhDCw3Imt2ofpadze8VdSuOvtkPdXkr8Ub0UB7vjMDvN0o6MRvWdhTVAIDKrs4Cc7no1E4nVCHzzUGRHCAMMk4Y7HcI+5DpopmBKJmcTN+RXuiTdFrXDviCgpUSWhRxPr7AtLO0zPcyEEwrIQlhn4XzMpMs/tXctli/7KQ5s/YG9ZIX7poIBSXzULD2xi/gdPcM+yF6hyfNhROCtCKsXhihIW5m8Mye54dRnP7FmD04oJtW2JVKpBr/XGXguHnafCe1oLsOtUAVYrz2vS8bbK3vFX01H+WBOzh2NY4Q3Qs2JSiEvr1+B1KW0Kti4M73NWkuObnw85RVRz7tCR93aOQhJnhR81MjDwmK17/NmZEIaJUoLYa/6HmEu+jm/1i/i3vo+sKkWYLoz0HrjHXo8rb0ZgfRNRi0DRqATD0OI+kshWihon+oRpW/O37Z/w+M4minvPYNXJA3z+4//yxIybiUNEWQRe8fy+dWFZvnZgM1/KmxJhf1qHLSWWYXCwopjn9q5jQ9FhKm0fcZaLYWldmd93DLnJma3qkFTTiqFx1ba/1RHZgFgVhDve13LHh20bbRiuGDJyL6Ng+6sh22YNurrRa4hhWJw6siZsn8oOr0ZMar9PIDs6WrxHIX752UlVIJotJhUYjE0fyr93h5c7ODJ9UFh2nZnaE6WIS8Yz9XZiZnyx3nbl2I0W+tR1ZCk6iHNkG8pfg4hJxNVvAnjiQal2lV/elihHBvLuq2sCvcs9bkSMB+U4zUaQhWUGctZ94QkTER/XUfRAi9jSYenxvUEJ91oOVBTz/VWv8eiUeW3oWegoBW8c2hqWbZG3kqXH9zAtu3+bDiIKFls6FNZU8tO1b7Ku6HCD7fvKi3j1wGaGpubw8zFXhp2zn+DyUGmH99Q1weWpKw4OFyEMkrqOpuzo2rDsk3tOQpznwXeRJHv4TRRsf42QTkDCJHvYfGgiABRqF58zsZvKo9dEBVq8Rwm1uaSnfOUsPrKUIm8JhjDIic3i0m5TcRuBFI2zi8Ysw2RC5ki6x2VzuCr0HuTzel+JLW3dbSZMGitCPfu1wIAe8G//AO/KFxrmy1seXEMuxjP5c5jZAzp1JF4pBZXVOMvWY6/YCKc+u/iIHtmYU0Zhjh58On+7kRskpTBHDMRZvSX0gwswRg+CCE4/jWYsw+Sp3atCtlt18gB7ywrpk5gWNUWs5X4vFf7w0zAOVZYSDY3XbOlwrKqMO5f+H8Xe5osYt5Qc446Pn+FvU2+kT2JaSAJeKsWErN68diC4uSBnM7lLH6SSGK0Qz9KxyR42LyzxHpcxgMQuQ8M+drQhhEFcen96TvwaB1c8HLRd3xk/wJ3QpcmnIE21jwyG1thq2h6t2KIARznsLT/IE3te4oNjn2Kr+r1ef7/1n1zRbSafz51DuielgdC2pc3c3lfwx23/Dum4GZ40LsyZiNmBohfRhpISpE3l89/F3rm08UW2F//GN/FveouYWfcTM+lz59bJKML5eA32Gx9DIznI6tBx7OcWYb/xMe4vXg89shs+qVAKc9qYsMS70b8XRnpKSDaNdRGKts5CjSGVYm9ZIVtKjoVl//zetXx/5KUR9ip8gu1T3hS2dFBRkD7tKMU9y19oUbjXUuav4evLF/DixV8kIcTo+419x4Ql3jNjEpie07/V07kN0yKt7wW447PwVYaWf589bH6HmwgqhKDbmDsQhsmB5X+muQi8MCz6zPgBXQZf2+QaJR3is4ZQejD4J2tnkpA1BKVkpw4mRTP6WznPSCV57+hybvvk27xzdGkD4Q5QaVez4MAiPrfkPnaW7W9QMGYZFjf1vYqJmSODPq7LsPjt2O8SBcGmjo2Ayue+07RwPxOlqFn0EN5VL6KioFBIOf7P/meHnx8b1LGUwn73U+zXPmxUuNejogrfX59DHTqOOitHXRgGRvcuGP17huaAAPPCCQ3216S/0kE5Nv6t71Hx5N2U/XE2ZX+4ivJ/3I535fMob1VUfIdN4SjJe0d2hG3//tFdre7zHUkSXB6MVhQvpnriznvpoy0d3jy4hWNVoRVcF3kreTl/Y0iFxIYQ5CZnMiKtW6huckOfkRFLLVMoBl7xUEjFmun9L6XL4Gs7lHCHQIEpQM7Imxl16+vkjLwFy5Ncb40rNo1uY77A6NveIGvQNc3uTxgm2UPnQpjBuezh88Oy05wb9JCm84gtbVYXbuYbq34edOQo0RXPU1N/T9e4LvUek0olsaXD99b+jiUnmn8UnmDF8dC4HzIqfbCOurchyrHxbXyL6ld+FpqhaZH0ncUYcSlt4ldzBFJ8BHgr8W1ahDx1HJREJKTjHn4FRkIaSjoRHd6hHIk6cBTfI/8XmmFCHJ6f3I2wGklTqvHh+8szqILioHZlXT0Tc+a4oIvwvKtfouaDx1CVJY0vcMXgGT+XmEu+Doioq2VwlOR3G9/j5RC7s9QigBWzvx1VAv6bn77EJyf2hWznMkwWzfoKyVHQLvJzHzzJ7rKTIdt1i0th4SV3hlREakuHEl81t330FIU1weU3T8rqzR8n3dDqqPuZKOlQUbCVHW9+E7umtNm1mXlX0++CH4MwOkwLQyUdQFG0931ObH0ZMMgcdBXp/S5CCIOaU4dx/FVY7gRikrsTOJ8Ef/7d+da3Kd7/YUg+JeaMZOj10TVYUFMfLd7PI0oprv3g7pBz1ad1Gcefxv+oweuBHESDTwvW83z+myw7sRbJZzcF2bEZXN9rFnN6zSLeiut0g0nOB+WP3YxzLPQIZ8yFX8Ez7Y5zmn6hHBtZegzvx4/j2/IO2GflEBsWroHT8Ez/AmbOwIgKeN8TC5GbQ+/5bM2fhTl2SIP8d+U44PPjf/p15I79Te8g1oN1zQVYE4YHfczqD/6G96N/Budf3gzib3ww6iYV2lLypy0fht2hxRQGK2Z/K8JehY8tJWsKD/L15aEX7l/aLY9fjr3qvIvBgxXF3PBe+ILpyRm3MCQ1JySb2uLYry1fwIGK5m90L+46kJ+PvRJTiIjXOkhpoxw/Bdtf5cSWF6ku+exv1jA9pOdeSvbwG0nIzKubDt0RUEpRtHsx+cv+gL+qqN4205NIRu4sYpJ7EJfen6SuoxHCCOlcoqSDv6qITQtuxV9VGJSN6U5g2Jyn8CR3a/cDsDoy+ps5T9jSYXXhxrCKTJedWMvJmiIyPGn1TmK1J9RxGcOZlDWKIm8pByuOUOP4SPUkMyCpN1IpLdrPAUpKnOO7whLuAN41L+OZ8YUIe9U0yrGxD22k8r/3g7eJDgXSxr/9Q/y7lhJ33c9wDb00IhFlVV6J3LonLFvnk/WNCm9hmiiPwP2luciCYpyla3G27IbKarBMRFY65qQRmGMGQ5DvoTZNJljhDmDv+Jiad/5MzGXfjCrBoVD0SEgN275nK2zbAsswmJjVmwHJWewKoX+5KQxuzR2PVArzPH8/BdXhdwYJ2JczOCU7pN+ZZZhkxMTz/EWfZ8mxPbywbz1rCg/WbfcYFpd0H8j8vmPIS+nS6g4zTWEYFhgWXYbMIWf4jdSUHcWuOYVhefAkZmNYsdTm6kTT31FrUEpyfPML5C99sNHtjre83gTauIyBDL3+XxgiJug8dGGYWLFpDLnuH2x77av4ypvXG1ZsKoOueoSY5G4ILdyjGv3tnCcsw2RB/qKwbCWSBfmL+PKAmxpNe6kV5+meFNI9KfW2GR3jvBf9KAfncHidHABU2QlUVSkiPi2CTjVxLMdGFuZT+cx94AuiUM6xqXr5J8THJmH1Hd+qpwPKcXC27Q27P7s6cgJVWY2Ib5jyUHtjITJTsa67CNcNlzQ4dnNtJxvsz7So+fhfIfvoXf0SMTO/BDEJIdu2FS7D5KoeQ3h468d4nYZ1Ni1xba/hreox3hbYUvLnSTdwx8fPcKK6vMX1BoIfj7qM3OTMiKaBhEtrfQjXvvY7nJbdjwu6DqDcV0OprxrLMEnzxOI2LNRp4dzWaVK1eewxSV0hqetZWzvOxUtKm7Kj68hf+vugbaoKd7L7nR+Sd+Wfgtq/YVhUFu7EW36CxJwRjJj/PCe2vsiJrS/jLTtSb707PpOswdeRPWw+piehUwn32s/qTJS0o/4ziG7vOjh7yw+2vKgJ9pUfwoyiC6fmLJRC+cKffAugfDUQHyF/mkGYFtWLHgpOuNciHarf/B1J973SuoMroLp1kxZVjbdR8V6LECKQx3/26yEIdyUd7IMbkSebScFpCn8N3rWv4Jl4Y1R1oYm13FzaLY/XD4bWmSfGdHFt7+FRJdwhEH1Pccfy1Ixb+cHq1xrtkV5Luiee74+8hOnZ/aMmbz8nLqlV9t3ik1sVla79PhPdMSS66xeQig4knKMBw7A4uvYJQq38LclfSnVJPjEpPRuNvivpoJTk5I7XOb7lRaoKdwKBFJxuo24na/C1dB11G+XHNuAtPwZK4UrIIrnrGEBFvWCNJLW1BsV736dw1yJ8VUUIYeBJzCFr8HWk9JjQqLCPFqLTq06CT4bfwcMrwxuuoTlHCIHwtE55C09chJxpGqUUsuQI9v7VIdvK4kP4967E6jO2dTndrtadhkQr7YM7iIF/41thm/s3v03MlFsi6FBk+PKgKXxyfB8lIdy4fSlvMjGmqw29Ch/LMElyx/L3aTexp+wkz+9dx7IT+yj3e/GYFv2TMpjTZxQX5OQCbR9JDoXsuCRGpXdnfTM3HU0xIDmLfkmZbeCVJtIopfCWHeHU4dDPuQDHNj1Hn+nfbbhfaWN7K9j++j1Unqyfrul4yzm44hEOrfo76f0uIq3fhaT0mIzpSexwXXtaorZm4tjG/+Po+qfwV9ev9ag4sYWiPe8Sk9ydnhO/Rlq/i6MyVatzfWtRRpIrgYKaopYXNmGriWIMC6v/xEDEN4yacCOz77npNqMUvtUvhuUjgG/VgsCE2HAxA60dwyY+FuLb/iZHCIGsCO9vFWiVbVthCEG6J56HJ8/ha8sXUBrEk6Lbc8dza+74c+Bd+NROSe2bmM4PRl7aQKDb0sGMsu4/EPBrbp9RYYn3OX1GRl0ak6ZxlLQp3P024fbbLNr9Dn1nfL/+PpVCOn62vfplqoqarh9S0k/h7rcp3P02wnAxePZjJGQPi9roclsghGDvh7+kYNvCZtfVnDrMrsXfp9eUb5Iz4uaoE/DRdwbrJDjSYXp2+BfB6V3GY8vQc1U15wYhBGZqN6y+4X3HnvFzUGHkIoeKMAyccFJBTuMU5rfu+EJg9OqKyMkIy94cP+ycpcK26ulCFORUN4ZlmPRLyuDpmbdxdc+heJq4iA9OyebXY6/ma0NmnGMPw8cQRqOR9WgVuJZhcmHXAQxKCe1mtm9iOlf2GBK170tTHyEE/qrgWtg2hu09dTrl4wyU5NCqvzcr3M9GST+73/tRpxrCpJTk8Np/tyjcz+TAsj9Rsv8j5Dm4HodC5/nWogzTMJnX+4qwiozSPSlclDO5waTV84FUssFYcX1TEUA5Np6JN4Vu6EnAPeqac5cffXZLyJBsW5++pRwHc/Ko0A2FwJo6utF89kijpMRIyQ7b3kgJrYXfucQyTLJiE/nxqFksvvyrfGvYhcztM4prew3njtwJPDPzNv4z81Yu6Jp7vl3tFDw8eS59E9ODWts9PoVHpsyLeOtGTduhaH0g4Gx7pRxObn8t5F35yo9Tkr+sbkBUR0c5fo6u+0+oVhxa9feoSy+KLm86GZkxaVyYPYl3jy0LyW5u7yvOe/mQLW0sw2LHqX28dOBtDlQcwS9tUt1JXJgzmVndp2MKo1MPgRJmoC+6e8J8fCufD87IsIif/79gudvWuTMQsSmtsG1dkR0ECkfNicNx1m1H7Q8+ZcC8ZBKkJJ6zx5nuMdfj/fTZ8GxHz0Y5dlQVrJ5JbYQ63uXhhj4jEQgMAY5SdQEGHdlte0zDIN7y8MSMW/jj5g95+/B2apyGtVFuw+SSbnncP+xC4ixXXaqQph2gFJ6k0Cfb1uJJqB9EkI6fwt3vYHtPhbW/E1ueJ63PtLD9aS9IaXNy55s4vtBbslYV7ab8+GYSsgZHzcyO6LySdBIcKfnJyK+zv+Iwe8oPBGUzrcs4vpg797xGWhwl2VSyk4e2Ps6OU3sbbF9asIY/bvs383pfwZcHBiLPnTkyFHfldxGWG++yp5tf6I4jfv5vsfpOOGcTOZVj4xo0A//Wd8Oyd+XNjIwoFQL3XTfg++dLQQl484LxuGZNbd0xQ0AYBmZWX8yeI3AOhjaVVMQm4x52WdQK97NxnXFx0q1lzz2WYWAIix+MvJRvDruAVw9sYn3hYSptH/GWm6FpXbmu93ASLE+gP70W7u0Kw3SROfAqDi7/C9IJ/aln1uDZ9VoZCmFQcTz8tsQVJ7aGbdueMAyLkzvfDNu+YMdrJHQZGkGPWkf7uJp0UEzDwI2bx6f8L99e8xtWF25qcq1AcFWPC/nR8HvOoYcNcZTDh8dW8j/rHsJWTT9qK/NX8K/dL7CrLJ/fj/tBh5qKFw6xl92He/RsvCuew7fxrXptGY3UbrjH3YB77PUIV+w5E+5w+unAkEsQix5CVZaEZmxauMfPiYgoFYaBcrtwf3U+zvINOMs3oE6cVeQpwBjQG3P6WMxBfVt9zFBRjk3MRfdQ+Z+vQgiPmWNm3glREq3RtA9qgx1xlpu5fUZxU7+xddvOLEw934OlNOFhuuNJ638xhSGKSWFYdBk6t15LR2GYOHb4bYkdfwgtgts5vorgB7g1tD0ZVRpGi/fzjGWYxAoPf5v0C7aV7uG5/W/w7tFP6tpIJrriuar7hdzY5yq6x2efVxFsS4ddZfv50frmhfuZLDmxit9t/js/GP6VNvYu+jHSexF75feInXU/svQoyl+NiEnCSO0G0jl/kVkh8IyfR82Hfw/JzDVsVkQ74tTetJiTR2JNG4Pcdxh5+Dh4/RDrwRjcDyMtGeXIiB0zJP9MC6vXKOKuf4Cqlx8ISsB7ptyKZ9Ln2tw3Tcfl7HQlnb7UAVCSHuO+RMn+j0NK48gZeQuWp36qopIOpiv8jlumW3euC47wugO1FUKdXW2oOW84SmIgUCiq7BoMIYizYutej4a7vm+u+hVLTqwKycYUBm9f8gRpZ0171UQPSkkq/+9b2DuXBLXe7DqYhC/+C0xXmz0pUFJ+NnlVAIYRFX8DfulgEugaYXsrcQ5txl61AHvXJ6ACNxZGWg880z+PZ/Ts8+usRqOJSpS0KT+xlR1v3BuUgM8cdA39L/xpg9eltCnZv4Rdb38nLD/S+l3MwFm/Dcu2vbHl5S9SfmxDWLZZg6+j78z/iYprEGjxrgmBkzVFXPHunUhCj3zelTufuwbM11NhoxQlJaCofv03+Na9WidCG8MaOJ34eb8Bw2o3edytRSqJQFBp+3j1wGYOlBfhkw5J7hguysllREYPfBXFqJ1LMFJycPWbENUFqhqN5vyjpE3NqSMc+PTPlOR/AsppsMaT1J2uIz5H9vD5TT55V9Jh3VNX4qs8GbIPQ677J4nZI6KmELOtkI7NyR2vse+jX4VlP/SGJ6OqYFWLd01Q+KXN03sX8uiOZ8Kyz4nN4o2L/xlhrzSRpPbCIE8dx7vyeXzrXkNVlQY2uuNwD78cz6QbMTP7oqQ8p7n55xNbOhR5K3ls2ye8d2Qn3kbSZfolZvC5/mO5ptcwlJKdqneypmmkkjhSBdoDEqhziqaprprzj5Q2hmHhqzzJia0vU116AOX4sGJSSO93Eck9JoJy6uW5n42SNkfWPsmhVY+FdOzYtH6MvOmF1r6FdoO0vax54tKQO87Epecy4sbn2sir8NDiXRMUjnJ4cPM/WXBgUVj2ljBZedXLEfZK0xYopUBJhGGibF/gv10xKCVB0WlEOwSE+/7yIu5ZtoASX8uFXdf3HsEPRl56DjzTRDO1RaUbi46wpeQYNY6feMvN5C596JmQpqehahrlzC4ygfOtbFa0n2277bWvUXZkdVDrTXcCw+b8B09y904zYVUpyZG1/+bQytBucgZc/iCpvaZHVa/36PFEE/WoVhRstMZWc24RQsDp/vzijH7zQhjnbJppNCCVosRbzdeWByfcAV7O30iKO5a7B02NmtxIzbkjMLQOXsnfxIv7N7C3vLDe9oc2w+iMHtzUdwwz9dArzVnU6yIjjNAmMwuDQVf9mZ1vf4/SA0ubXeqKy2DwNY8Sk9w96JuDjoAQBt3H3om37BgF218JyqbXlG+S1ueCqDufd55vTdMqpFJkxgQ39a8xMjxpEfRGozk3PL7zU4q9obVSe2r3Km7qN5ZkdwwoFWiDqRRIiTB1tLU5pFLU3upX+L1IpUhweXAZJo6UUd3TXCqJz3G4f8XLrC482OS6dYWHWFd4iDl9RvLd4RdHnSjQNE1tiguAY9dgmG6EMJCOfd6jskIYYLgYdNWfOHV4Dcc3P0fx/iX18ujj0vvTZeg8svKuDNQstZFwP/PzUNKpyxM/87/PF0op+l34Y2JTe3F0/dP4q4sbXReT3J0eE+4hvf8lUfk3qtNmNEGhlOJETRFXv3dXWAWrn+8/h68M/JwuWNW0G2psP5e9/ShVdsMJly1x96Ap3N5vPOrNJajySoRpIjJTMSeOQCTEdaqagWCorbc4WFHMc3vX8dahrVTaPiDQrWpGTn/m9x3N6IweUSviHSW5d/mLrDoZ3MA9gJv7j+W+oRe0oVeaSCCljRAGJflLOb75BU4dXlVX1B+XMYAuQ+aQlXclwnCdd3EKn4lkf80pvOXHUNLGFZtKTFK3ejcgbXFcpRxO7niTE1tfpKpoD0raGK5YUnpOIWf4fJK6jm5TH0LxFRRFe9+ncNcifFVFCGHgScwha/C1pPSYWC+NKdrQ4l0TEveu/DnLCtaGZGNg8NYlj5MZo6PvmvaBXzq8eXALv9rwTlj22bFJvH7Zl7FXbEQdK0RVVSN35kNVDcawXKwrpyNSkxFm9InQc41UCqkUv9rwNm8cbH7a49DUHP406QbiLXdU5Yzb0mHx4R08sO6tkG1fvOiL9EhI1YWsUYqSDlXF+9j51v14y482uc50J9B35o9I739RpyxYV0pRuGsR+5f8ttmC0LiMAQy8/CHcCVnnXcADjd5IRLNor6Xz/cI0YWNLh7ty52OJ0H7U1/S8SAt3TbvjUGVp2LbHq8sCEeJRg7Aum4zrpivx/OQruG66AnWqHN8fn0IdPn7eBk5FG99d9UqLwh1gS8kxPv/xM1Q7fhwZPZ+dZZgs2L8+LNvn960j2gbAaAJIaVNVtJstL3+hWeEO4Pgq2P3ODzi5443TrXc7D0pJTmx5kT3v/bjFTi5VhbvY/OJt+CpOIEOYVN1WNHYDEe3CHbR414SAZZgMTs3lgZH3YgYZWZiUOYofDPsK+gGPpj0hALuVF2BHOtivf4T3Rw/j/Z8/Y7/6AaJbFp57b8G65gJ8j7+MKi3rdBf6M3Gk5MldK1h6fG/QNocqS/np2reiKnVmT9lJtpYcC8v2rUNb8Xfi30BUIx22v3Ev0h9s3Yti30e/orpkf6BbTCdASYfKgm3sX/q7oG3s6hK2v/71TvmEIlLoT04TEqYwuKzbNP4y/qf0TejR5Lp4K5Zb+17Lnyf8GENEx3RYTduiHKfuJk1J2a6jylJBqic2bPsY04XbcqGqagIveH04y9bj+92/8T39OubowbjmXor9zjLohH8btjxdRCcIK2L9yfG9HKs6FRVBAUdKdpYWhG1fafsoqC6PoEeaSCAdm5M738RfVRSSnZI2xzb+t428ij6EYXJk/dPNDvZrjJrSA5TkL42K6Ht7JPqfDWiiDkMYjM0YyoILHmF90TZeOvA2ByuP4pc2ye5ELsqZxNU9LsJtuBBo4d6RUVIGxKfPj7NyE+roSZTfRsR6MAb1xRjUL9CruJ11WXEZBpd0y+Ov25aGldBwUdcBKCmR+UcabJPrt+OvqMJ11xxUUSnU+CDW02qf8ZzedwAAsB1JREFUox2lFJJAfvs7h3cwIr0bO0tPUFhTGfq+CKSbfH3IDMzz3L9UofA6rRMgNU7oRdGatsUwLY5vWRCWbeHuxfSe+m1Md1yEvYo+/NUllOz/KCzb45ueJ63PjMg61EnQ4l0TFtbpnLDhqQMZlT643jZb2nXbNR0X5ThQ7cV+82Oc9TvAV1+AOMs3QGoS1pRRmBeMPz3gqX3cyAkh6BafwrjMXiF1D6llfu+R2Nv2QmnjEVW5+wD2oqVYs6birN6MOX44wmpfNzih4CiJUoq/71jGwvxNlPmqWTH72zy+c3nY+/zk+N6o6NRiCINEV+tuvhJdMRHyRhMpfJUnqSrcFZattGsoOfAJ6f0uioruM22FdPwU7/sQFWb0/NThVTj+akxX+E85Oys6bUbTKhpr/aiFe8dHOQ6qpBzvH/6Ds3JzA+FeR0kZ9hsf439iIaCiIs0hWGzpcHvu+JDjuqPTuzMovStqWfPpIM6nGwN94BPjO3TqjFQKW0ruWb6AJ3et5JSvmjjLjSEEZT5v2Pst94dvG0kMIZjYpTfuMEVar4Q0suOSIuyVprXYNadaZ+8tC1vUth9UKz8nhe3VKWPhoMW7Jmjs0yeigupCtpTsYmvJbgpriutt03QSfH78f3u+ycjy2cgte7Cff7tdpVBZhsm4zF7cO2Rm0DY941P53bhr8O8+gNyV3/ziGi/Oum0Yvbt16Mm1hhD8cPXrrCs8VPea73TOu6cVg21aYxtpEiwPF3UbGJbtnD4jP6sB0EQNwmrd0xTDdOOrLu7gOd0CYbpatQejlfadleg5+2miFvv0MIN3jy7jhfy32FSyo972cenDmNfnSmZkTwCl9CCmDo5yJPbHa1DFoUVcnNVbMC+cgMhMa1fpM7fkjiPR5eHPWz9qNto7PrMXvxlzFbHF5ThPvBJU9z+5dS/WxBEo2+mQoRRHSXaUnmDJ8T31XvdLhxJvFf2SMni3YVlAUPRLzIiAh5FBori531gWH9qODKFKIsHlYXavYVHVs14TwBOfhWHFIO2asOxjU3tTVbSHmMScCHsWPQhhEJvaJ2x7y5OE5dFPncKhA14uNJHEljbFvlJu/Pg+frT+Dw2EO8Dqos18Z83/cvvS71BuV+koUkdHgLNiU1imztLQBnxFC1f3Gsrbs77Kj0fNYkhqDvGWG5dhku6J54beI3lh5h08OmUesQdO4DzyLNQEl9JR140mCoc1KalQ8nT3IK8PVeNDqUDqU+3rLWEgeH7vuka3vXVoG7N7DQu67ezZzOkzKmrONaYwyE3O4pvDgs/BdxkmD024DpdOM4xKhOkiY8DlYdnGpfcnMXs4J7a8hLfiRIQ9ix6EYZLScxLuhC5h2WfmXR1hjzoP+qyhaRJHOpT7K/n8J9/jePXJFtdvP7WHLy77Pk9NexBBTFT1YdZEBiUlcvt+KGt+EEdTOGu3YV17EbSzSKMhDNymweU9BnNNr2H1tknHwdm4E++yd1D7QwsjC1fgc4imdCKlFEIIVGExzpK1OGu3gdcX2Oh2YY4ejDl9DCI7AyUlopm/c5+0ef/ozka3vbx/Azf3H8vMnP68fzS0wsCcuCQmd+kTVZ+bIQQ39huDx7B4cPP7+Ju5sUh2x/Lg+NkMT+8W9s2Lpo1RkDP8Rgq2LQzZtMvQudSUHaX04DIcbzmEKW7bBUrSZfD1HFr1WIiGguzhN4H+/YeF/tQ0TWIaJj/f+EhQwr2W/IrDPLj5n3rUd0dFKlRhSfj2Xh9UVUfOn3OM66ybDqUU9svv4zzzRsjCHTgtgKOniFdJCY7E959X8f3v44GOQbXCHQItQVdsDPSrf2IhOLLZIVPF3uq6/PazOVhZwvIT+/jakBmkhtBSzxSCH4y4FCdKi59n9x7O27O+yj2Dp5FzViFqXnIXfjTyMt667G6GpWnhHs0IwyAuvT9Zg68LyS6hy1Cy8q7ixJYXQUkMq2N3UhGGRc6Im4hJ6R2SXfbwG4lJyomqG/D2hD5zaBpFKUVBdRGfnFgTsu3io0uptIOdSKdpd7R2GmRHmiapFNbYIWGbm1NGR02xqlIKlML/9xeQGxuPlp+J3Lwb32PPB27omhDSsoXBLb9cvxiXMPjL5Dmke+JbPKbLMPn5mKuYkNULK0qf7BlCkOSO4Zb+43j1ki/xwZVf563LvsJHV36Dpy+4jSt7DsFtWlHrv+YzlFL0nfEDMnJnBbU+PnMQeVf+ifLjmzi28b+YnkTcCVlt7OX5x7A8DJ79GDHJPYNan5l3Nb2nfquNverY6LNHC5ydUymVbPGC1BGQSJ7PfxNJ6O/VJ/28dGBx1OSjaiKIABHfikiSAGI7Tk9rYRgYfbohckIvnjT698TITI2eyJMC+42PkXsPtby21iT/CPYr7ze5PcUd2+y9ycmaCu5ZvoAkdwxPzbyVW/qPI7mRnuduw+Ty7oN5asatXNRtAEY7iFhbhokQgkRXDJmxCcS73HWva9oHQggQBrmX/op+F/yYuIwBja5zx2fRY/zdDLnun1QV72Xnou+gpE3mwKsQ7eC32lqEYeGKS2PY3KfIGXlLk0Wocen96XvBj+l/0QPn1sEOiFDtqfHyOcSWNqYwWVawlsVHl1LiPYUpDLrFZXNDr1n0S+rZ4YcR3b70O2wpDW9IxeSs0Tw84acR9kgTDahqL94HHgV/6C3QjLw+uL80tw28On8ox0EdOBaIQjtB3rB63LjvvRmRlRY102eVz4/3p4/WT5MJBpeF52f3IGIab6137/IFfFqQ3+wu0j3x3D1oKrO6D0IIwcfHdnO06hSOUqR54rio60ASXR4kSqeaaM4p0rGpOLkdd1waMUndKD+2kdLDK3G8FRiuWBIy80jtPR3HV8GJrQs5tOpvKOkHBKNufRVPYtfouUFvYwJP7yRKORTufoeqot1I24vlSSC193QSs4cjpY3RgXXTuUKL9yZYkP8WT+1ZyNHqgka3D0/N4xuDbmdYWl6HvZjM+fBr7K8IPgp3JiNS8/j31N/W/VsqiaMclAo8Vu7INz0dHaUU/mffQq7ZGrKt684bMAb2jhrBGimUlMht+/A//VrLNzUxblxfvAGjdzdElHSZUY6Ds2IT9kvvhmVvXXMB5rTRDb5XW0pWFOznmyteDmo/iS4PV/UcysycXLrHpwQi90I0qDXQaM4lSkq2v/ENhCHoMuQG4tL6YXoSkP5qvOXHKNj+GkV73q3XVrLbmC/Sc+JXz6PX5xfpBAb3CWGglEQIo0NPmz3XaPHeCP+7+e8syH+rxXWWsPjFqG9yUdfJHVLA37rkW2w7taflhY0wKXMUj0x8oO7pxNrCzewuO0CN9JLsSmRal3FkxKTiSEf3hW9nKClRJ4rw/eGp4CPNgMjJwP3tz3fYKJSSElVQjP3+ikDOuH3WZ+N2YY4ehHnRRERKUtQI91p8/1iA3LE/LFujf0/cX72x0W1KKW7+8D/sLgu+8D3WdPHshXeQE5esi9815x2lJMrxseOtb3Pq0Kctrs8ZeSu9p9zX9o5pOi06/HkGUkme3PNSUMIdwFY2P17/R7rEpjMkJbdDRZOlkgxJHRC2eB+SkosjHV7IX8SL+Ys4UFm/E4cpDKZ3Gc8tfWczIm1QhxV0HRFhGNAlHddNV+D/vzcgmG4pyQmBdBkpoYNF3WsRhgFZabg+dyVcfzHOuu2o8srAtpQkzFF54HaBUs22Vzxf1PWcD8u26Q5CjlI8PHkun1/yDMeqylrcl9sw+e342XSJTdLCXRMVCGGA6WbQVX+hYMdrnNi8gMrCs2eeCFJ6TiJnxOdI6TnpvPip6TzoyPsZ1DheLll8O1VOaK3sxmUM52+TftFGXp0fpJIcrDzKDR/eE7KtKQzeuvjfrCvayg/WPdjsWoHga3m3ckfuDeG6qjlPKKmQO/bhX7AYTjXd91307Y77ttkQH9Ph0mWaQ50ZeRdE/Xv3PvosKoRi1TMRPXPw3Hdrk9tt6VDh9/HTtW/yacH+JmeQ9kpI48ejZjE0LadDPs3UtH+kY2OYFhUF26g4sRVpV2O6E0jpORlPYrbO6W6C2s/NV3kSu+YUwnTjTsjCMAOF3J2hsDeS6F/YaWxp89rB90MW7gCrCzdxqPIY3eOyO0wE2RAGvRO6Mzp9COuKQsttnt5lPBkxqfxj13MtrlUoHt7xFLFWDHN7X94uukhoAghDYAzsg+fHdyO37MFZth55tCCQ8x3rwczrizl9DEZOJsqRUZcm0tYIK7rF+pkopTC6pOOEKd6NLul1w50awzJMEl0e/jx5DkcqS3l+3zpWFORT7q8h1nQxIDmLuX1HMyajB7Z0tHDXRC2GGZBN8Zl5xKXnAgr4LJ9bC/f6SGmDdDi5802Ob1lAVeFnTTAMK4aM3MvIHn4T8Rm5zZ5DNPXRkfczaE2O9x39b+CrA2/uUPnbtnQ4Vl3A7Uu/wyl/eVA2mTFpPDX19+wtP8jXVj4Q9LHchot3Lv0Pia6Wez1rog/lOA0iy7V9w6MxRURTn9p8fd/v/h2WvfubtyG6ZQX1XSulGu0a40ippzJrNB0IJW0qi/aw44178VcVNbs2a9Bs+s78HxCGFvBBoM+UZ3AshEmiZ3O8+mSHEu4QiJblxGby98m/5P/Zu+/wKK5z8ePfKbur3jsS6qL3XowLtjG4V9wd2+lO77m5aTc9vyTOdZx+HdfYxh3jjisYML1XIQkQRRJCqJfdmXN+fwgpCFS2SazE+TzPfW7Mzjtz1HbfOfOe9yS54vs8PiM8hX/O/iW2FPxw84M+Xau9N/xbWML39oPKudddSYimaSpxHyQ0XUdPS0LLyfA9NjMVPSvN65+1pmndzqyrxF1Rhg4hLJprStn58mf6TNwBqnYvpfjdH6rE3Uvq3fI0gWy+ZA/RjZtM3SQ3KpPnL36YB0beSVp48lnHDI/M4Ouj72PJRQ8R54zhM6v/i5PuOp+v9eKBt4bUol9FGUykbeO44VJw+PA3aBo4brgM6UPXIUVRhj5N09n7xjcRHu93Wz9R/DaVO19G2GoSry8qUzpNUli81+UhZ8W64rCljaENrdl3aE/gY/Qo7s6/gU8V3MTWml1Utp5AQyMjIoVx8SOwhIWGxjNlyzjWQ2/8vhxtqaLFaiXcHDo7cCrKYKEZBmSk4Lj/BjyPvtL3Zk1OB457rkEb7v2suzKwLCEwz/jZ2FKgo6kZTqXfCGFRe3AVbQ1HfY6t2L6E1DHX98OohhaVvJ9iCYsFw+bxlz1P+RV/ZeYlaL1uBD74dWzrPSlxTOdTio4FpqZu4hEeGjxNAV2jTbgJRyXvinIuaLqOnj8c5zfuxn73E+wte87edMo00CeOxJw/Ey05fsgk7h01982Wm5NtzUgg3hlBpMM56OrxpZQ0eFp5oWwLyw7toKK5Hokk3hXBgsxRLM6bTHpELEJK1Y5TCTpdN6nY/pxfsc0nimmo2E5Uymi1qVMvVPJ+iqmb3JR9Bf/c9yweH+uuR8UWMCouv59GFpq66wojIeAFpxFmeEDxiqIERjN0SIzDvHUh5vXzsTfvRtY2gAQtNgpj8igIcw2Zxci2FBiazpaaIywp2ciKipLOMkhD05iTms/ivMlMT8kO+SS+o//Ew7tW8EzJRjyiazlTdWsT/96/gaf3b+DSYSP5yeSFGJoe0l+TMjjVHV7vd2ztodVEJo9EQyXvPVHJ+2liHFFcN/xyrzdp6nB/4c2dO4mez0zN4ILUaTy461G/4qckjsWpO4I8KkVRfNWZlIe5MKaN6/paRwvMAGZsxakk0y0s9tZW0eBpJdLhoigmhXDTAcgBaRtrC0Gr7eGba19mY/XZbTJtKVlRsZ8VFfsZn5DBgzNvJMJ0nlWKEio0TePHG9/gjfLe2/tKYPmRPVS2NPDXuYvR1Ay8EkTCagXp/zoY290Y0PvL+eD8zja78e2xn+ZocyWrqjZ6dfyXRt7FRWkzVP0g7bPx2VHD/OoND7A450p1E6QoISbY/eqFlBxqPMmzJRt58/BOmi1P52suw+TyYSO5NX8KRbEpQb3u2eMQeITNZz9+ln11fa/T2VZzlM+sfJpHL7wTDTPkZqstIVhSuqnPxP1022qO8Juty/nviQv6cWTK+UYznIAGPW7H1jvdDAfVxbxXqs/7GYQUSCQP7nyUlw+9Tavd/aKtJFcCXxp5J1cPnz/AIwxtlrBYWbmBb2341VmvDYtI5YbsBYyKzSPajKTVbqOqtYbXj3xIWcNhls7/u9qcRVGGuCeL1/GnnR/1+bH+qaIZPDB6Xr+NQ0rJL7e8wysHt/kUd0XmKH429ap+GpX/hJRc887fqWzxremCQzd484ovEOtUJYtK8GxdcjvN1Xv9ih2x8PfE5cxVG171QiXv3ZBSIoEWu4WXDy5n+dGPqWmrxdRNhkWkct3wy7k4fQZIhlxv92D5/c5HeLr0VQAmJozivoKbmJUymaa2Wg5UfkKruwHTcJEcW0hm4hjarFZM3aG+n6fYwoOumWiahpQCIW0MVVKkDGJCSp7ev4H/3fmh1zGfKpzBA2P6J4Fv8rSx4K2/0OZjWzpT03nzii8S5wqdZNcSNqsqy/jW2pf9iv/CqLncXTgjZMuBgkWeWsugnZokklKcWruhPneCSdgWx/e8SumHv/A51hmZwuR7Xu/8GSndU7c13dC09r4xkWYEt+ZeyZ3513Z53RJWe0tIVSnTo2+OuZ9ww0V1ay0/GP8Fjp7cxSuf/IA9R97HFp4ux6bFjWRKwc1MzLkaIWz0IL+RWsJC03QMTUdKiSVtHCF4Ry+lQAK2cLPtwOscObEdj9WC0xFBdvJUxgxfgIaGpnagUwYZISVHm2p5yIfEHeCx4rVcnFHIiLjUoD6Vs4TNywe2+Zy4A1hS8OKBzdxTODNkkl1TN3jvqH+znADvHd3HfSNmBXFEoUXYFrph0nyihKrdr9DWUAFInBFJJI+8mui0cZ3HKIHTDZPkEVdycPX/ttev+yB1zA0gBajkvVdq5l3pd5/s+zfLt/yBvurfhidP5ra5D2EarqAk8Jaw0TWNVZUbWVm1gQZPEy7DSWFMDtcNv5RwI5yBWhjXF1tY2Lab97f/iW0HX6fNc/YbXoQrjom513HhmM+ja0bQb3IUpb8IKfjD9g9YUrrJ59iFmaP56ZRFQb9h/dKq51l7/IBfsRMShvF/824P6ngC9dXVL7C6qsyv2NTwaF5b8Pkgjyg0SCmoO7yB8rV/prFyR7fHRCQWkjntMyTkXaImRoJECpua0vfZ9/b38bb2PTJ5FGNveATddPXv4IYAdZup9BshBZtLX2b5lt97dfyh45t4euWXueuivwd8XQ2Nf5cu5bkDb1DRcvysY/6y50kuy7iAr4y6mxhH1DldJCuERaungSc/+AzH60t7PK65rZbVex7jYNVG7rzwr5iaC30IbgqmDD0eIXjdh4WUp3vv6F6+NX4+Mc7g7v/QaLWdk9j+YgZwM+8YohMBUgqqdr9K6Ye/7LX7SfOJYva99R2Gz/wSw6bcO4AjHLo03SAhfz75839C6Qc/Q/bRgjsqdSyjrvoTWgg+FQ9F537KURmy3J4m3t78/3yKKa/ezIb9z2H72Gu/Q/t6Bcm3NvyKh3Y/3m3iDtBqu1lW/h53rvgmx1qOY/l5vWCQUvL0R1/sNXE/3ZGa7SxZ9XU09eerDBKlDdU0evxLeN3CZufJY0EeEYQFUCIRboTW+hMhBcOj4v2OHx7pf2yoErZF7aE17XXXXrYtPPTJw1Tteb3PRFPxjqbpJBctZPziZ0gZfT16N7unRyQWknfRDxhz/f9hOCPV+gMvqU9/pV/YwmJT6YvYoo8t1ruxseQFDD/vvjVN42dbHubDirVeHV/ZWs0X1vyIZrvVr+sFyhYW2w6+TkWtb/WqB6rWU1Kx2u+bHEUZSP4m7qfHiyBWeAopGBGb6nd8UWxKUMcTKA2NG3Mn+h1/Q+4ELOF/X+5QpBsmhz55uL1+2gfla/+s6q2DSNMNwuNyyLvov5h673IKF/ya3Au/T/4lP2HczU8y4dZnSR55DbrhUIm7D9RvqNIvDN1kY8mLfsWeaDjAwaqNCB8/TIQUlDUcZtnh932KO9ZSxdOlr2Kfgw8vQzfZsN+/baTXFT/j902OogykKEdgNayRDldQNxHS0Lglb5LfPQcW503G3x7W/UHTNDIj45iWNNzn2NTwaC5IKwio7CbUSGHTULmD5up9Pse6Gys5WbYCoSZGgkbT9famEc4IEvPnkzLqWpJGLCQyeRSAWijsB5W8K/2izdNEbdMRv+OPntyJ8GOHtmfLlvl1vZcPLj8nO7pV1e2nonaPX7ElFWtobD0R5BEpSvDlRScRaTr9inXoBmPj04M6Hk3TGBYZx7TkbJ9jxycMIy8mKSQWup/OEjZfHXsRLh8SIQ342tiLQ+opQlBoOlU7X/I7vHLXK6rHeD/RNB3dcKDrplocHIDQevdRhgzLDuwxucfyvYxFSMEbhz/y63rVbTWsqtwwoLPvUkpONBwM5AzUNR0N2ngUpb84dJ1FWWP8ir0ko4joAGfuu2MJm+9NuIwYh/cLYSNNJz+YeHlIlpiYukFBTDK/m36dVzX5OhrfHn8p8zOKQqblZbBomkZrg//vjW0BxCrKQBhaf7FDWMeCSrfw0Ohp7kwyz+VCy944zYiA4l2OKJ9j6jwNNNstfl+zvLkCgW/1kYGRPpcGnUnVvCuDg8at+ZP9KlO5NW8yoh9KVEzdID0ilr/MuYV4Z9/vV7GOMB6efTPDoxJCtsTE0HWmJmfz6IV3Mj+jqMfe+NOShvPwnJu5MXfi0J39FAG8l/vx1FdRBpJ6LhTihBS02m28fGg5Lx54i4OnSlE0NKYmjWNxzpXMS5uOlOKctjs8k8MMIyNhLEdruu+r25eC9Dk+13PbgbxZ034jNJBPjzVNJyosMaBzRAYYrygDQdc0siLjeWD0PB7etcLruLsKpjM2IaPfxmXqOvkxSTx7yad4pmQjSw9u56S7ucsxsc5wrh4+ltsLphLvDA/5WWpT18mJSuDX06/lZFszyw7toKK5HlsKEsMiWZQ5msyo+M59MIYqR0SC/7Hh6n1VCW2hk+0p3VpW/j6/3fEPWs8oQ5FI1ldvY331NtLDU3hw+g/IjcoKmRkhW1hMK7iFpet8T96HJ08mMdr3WtRoRyQaGtLPWboEV+yAf5hlJU0kKiyJxtZqn2NTYgtIjPZ9gZqinAuapnFP0QwA/rxrRZ9/pXcXTufLYy7s93GZukG8K4LPj5rL50bNZXVlKZUtDUgkKWFRzEnLR9c0dLRBM0ttnLrBiHdFcFv+lFNjB1tKzFOz8aHyWdEfhLBIKlrIif3L/YpPKrpC7biqhLTQnkI4jwkpeLb0Nf5n65/OStzPdKylivs+/h4lDQdDpozG0E3GDF9ApMv32Y8Zhbf7VQ4SboYxK2WSz3EALt3JxWkzz8HTC8mkvOv9ipySf5Mqm1EGnbsLp/PsJfdyfc6Es2qznbrBoqwxPHHRXQOSuHfQNA1D1zF1nTlpeVyXM57rcyYwL70Ah25gaPqgSdzP5NANDF1H13QcujFovw5f6LpJfM4FOKN8bwdqOKNIHnGlStyVkKZJOdSWmQ9+trDZW1/KPSu/41MNdpIrnmWX/hOnHhobiNjCouLkbp748LNeL2CdWnALCyd/z6/rWcLmk+Ob+eq6n/kce1Xmxfxk4lfPyQdbU2sNf3nzelo9DV7HRIcn88DCpTi62fRCUUKdkALQaLM97K6tpMHTSpTpoiguhSjThUD2WK+tKN6QwqJyx4uUrfytT3HDpn6arGmfQwvx8ijl/KZ+O0OQruk8VbLU58WT1W0nefvwipCafU+PH82dF/6NcGdcn8fPGnGP34k7tD8GnpMyhQIfS24cusld+df7XW4TqDBnDIvn/hGH4V0iHuaI5vZ5f1atzJRBS9d0dE0j3HQyOSmLC9MLmZI8nGhHWPss+CBO3Ds60RxtqmNbzRF21BylurWxy2tK/9N0k7Txi0kde7PXMYkFl5E1/fMqcVdCnpp5D0F17gYWLP8UHj+S8NGxBTw57/f9MCr/2cJCSIutZa+xseQ5qur2d77mckQxPucqphfeRkJUVsDXsoTNSXcd96z8NpVe1JEbms7PJn2dSzPmYGjBqQGVUiKRXfpA28LqdQGuEDaVdft4+ZMfcKLhQI/HpcWN4MZZvyE2MkNt0KQoIcQSNhJ4q3wXL5RtYVdtRZfXpyUN5+a8yVyYno+U/6lLV/rfkY2PcWTTo9juxm5f180w0sbfyvCZXwIk2iC+eVTODyp5DzGWsHnryAp+vOWPfp/jvQVPEueMCd6ggqQjgW1oOU6bpxHTcBEVltS+WUMQF4NZwqLe08h3N/6WTSd29nhcSlgiPxj/RWanTA7KhisdX9/JxiNsKHmOE/UHsOw2wl1xjBh2MaMz5wMaeg8LxTriD1RtYH3xsxyp2Y7basFlRpKdPIVphbcyLHFsnzcCiqIMLEvYVLU08sDq5zjcVNvrsWPj0/nfWTcRYTpDvnPNUCGlQNoeju99nao9y3A3VoKUOCKTSC5aSMqo69AdYYM6ae9uga1adDt0DenkXUqJQHSZUbWEFVItFc/kER6eP/Amv9/5iN/neOWSv5EV2XVHQikFUsoeE8ehxhI2pm5Q0nCIZ8teY3XVRho8Tbh0J4UxOdycs4h5adNBSowgfE+EsKiuL+OdLb+nrGo93W2dHulKYGrBLVww+tMAPX5QCGGf9XOSUiKlOG9+fooyWFhCcLKtibs/epLq1iavYvJjknh03h24DDPkdmodyrpNcIU1qEsQT78xqd73Fu7majRNwxmVTsqoq0nMb5800tRnx5AyJJP3jgS9sqWa5w+8we66ElrtNqLMCKYnT+T64ZcRZoTBGaUNocAjLJYeepdfbf+r3+d449JHSA1P6pyhrTi5hxMNBxHSJtKVQHbKVLReZoCHkvaFcZz1cw7mTZwQFuXVW3nm46/gsfreJKoo40Junv07tEHcwUJRlP94YNVzrDvu227J1+dM4HsTLhvSvdaV/iOlRNM0Dq//J0e3PNVjSZAjIpHMaZ8lbexNAzxCpT8NueTdEhZVrTX8dvvfWVW1qdtFn2GGi0WZF/HNMfdjamZI9bsVUrL95B7uW+Xfws0oM5J3FzyBDmwpe4WN+5+nsq64yzERrngm5l7L9MLbiHTFD+pZh3NNSEFt42H+ufwO3JZ3s24Ak/Nu5MqpP+jHkSmK0t+ElBxpquWGd//P59gww8E7C79IuOnsh5Ep54Pi5T+ket8bXh07bMq9p2r6laEgtKadA2QJiwONR7hr5TdZWbWhx24trXYbLx18m/s+/i6tdht2CG2FrGsaExJGkevn4s2rsy5G1zSe+ujzvLHxl2cl7gDNbSdZvecx/vLmDRw8vgnRQwcEW9jYUlDTVsux5ioaPB0dE0Kjm00o0NBYuev/fErcATaXvUx9cyVD7N5ZUc47z5Zu9Cuu1faw9OB21YFG8ZmUgvJ1f/c6cQc4svFRqnYvQ6jP7yFhSCXvTVYLD3zyE2rd9V4dv7e+jK+v/3nQuowEiyUsbsq5wq/YW3OvpvjICg4d39TnsW6riWdWfoWjJ3d22exHSkl160n+tu8ZFi6/l8veuYer3vsMF711B3et+CZvHP4It/CoJB5o8zSx67Dvu/hJKVi//1mk9K0dqKIooUPXNDZVH/Y7flN1eUg9+VUGB2l7OLb1aZ/jjmx8RD1pHyKGTPJuCZtnypZR3VbjU9ymEztZX70tpGY/TN3kxuwFjIkr9CnujrxryIxMY9Wef3kdYws3r3zy3+in3cD8a/8LLHr3Pv5V/Dwn2mq7HL+rbj8/3foQVy6/nz11pdgh9H0baLbwsP3QG15vQHWmLWWvnhfrDhRlKGu23H7HNgUQq5yfhG1xfO/rPda496a1rpza8rXI8/hze6gYMsm7pmm8fND3GVCAJWWvh9zsh47On2b8mFGxBV4df/3wy/n66Pv4eNcjHK3puT1id042Haa0cg1CWPxlz1P8Zc9T2H3MCNe46/jcmh+wu67kvJ6Br28+5ndsc9tJbOEJ4mgURRlo4Yb/O1qHm6GxG7YyeOiGSfW+N/2OP773ddV5ZggYEsm7kIK1x7f4POveYUXlOpo8zUEeVWAM3SDKjOCROb/iiyPvJC08udvjxsWP4JeTv8V/T3iAqrpiPtjxZ7+ut774OXTdZPnRVV7HtNpuvr7u54hztDNpKAi0Zl2VzShDVXdPM0PpCWcwCCkZm5De94E9GBufjiXUe4DiG3dT3xsQ9sTTfCKII1HOlSFR/GRLwbGWqoDiT7TVEumICOKoAmfoBgYG9+Rfz70FN7K6ahN76kposduINiOZmzqVgphsLGEhpGD9/iV+X+tozQ4AhkWkcqjpqNdxNe463j68goWZF4Z0//x+ISEyLNHvcKcZiWm4gjggRTn3bCkQUvL24d28Vb6b6rZGdDTSImK4evhY5qUXwBDaYXRx3hSWHtzuc5xDN7ghZ6LaqEnxXUDtRVVr0qFgyGRbgTbtkCE8e9yRFM9MnsT05AloaEgpO0t9Ol73psd4T9xW+5OHcDPM59jnDrzB1cPn+33twcowHIzLXsR72x5C+tGxaGz2FUgpBvWufsr5o2Pjs5q2ZurcLZiaTlJYFGGGiUCin9ol+cnidTxVvJ46T2uX+OL646ysKCE5LIrPjZzDtTnjz9FXEjy6plEYm8zY+HR2nPSthG5+RhHRDnXzrvjOFZVGa61v+wp0xkandfaIVwavIZG8G5pOSliC3/EaGgmuuOANqJ+0J+vd16oJaeM0I/0+t8sRBeBX+dCuuv00eJqIdvh//cEqKiyRoox57D3ygc+xMwpv74cRKUpwdZS6vHNkD8+Xbu6SpLp0k/nDRvDFUXNJjYjhZ5vf4tU+ZqGPtzby8y1vU9nawGdHzunXsQ8ES9j8ePIi7l3xFI0e7xavp0fE8M1x8xFIDDUTqvhA2BYpo66h7vBav+JTRl8HUkCIddlTfDMkpvx0TWd2ymTinDF+xc9KmTToE08pJTkp0/yOz0mZipCCgz6UzJyuyQqtNQMDRQiLuaPu87n9VlHGhSTF5KpZdyWkWcLmaHMdN7z7f/x44xtnzS63CYtVFSXEusL51941fSbup/vnntW8Wb5z0NfBm7pBZmQcf52zmARX36WX2VHx/GPubUQ5nBjq71/xkW6YJBZcihke73NsRFIR0alj1YLVIWDIvHNoaFybdalfsYtzrhz0HyCGbjIqc77fNdhTCxazpmoTFS3H/Yp36efnLoG6bpIWP5Jrpv0UzcuZjIyEMdww81dqsaoS0ixhU9nSwH0rnuZYc897Z1yVPRYkPLV/vc/XeGTvmpDr9OUPU9cpiEni+fn38YVRc0kNjz7rmNzoRL41fj5PXXwPSWGRQ+LrVs4Vjaypn/U9ZsYXEfb52x1uKBkSZTPQPvt+e941vHJoOXWeBq/jRsUWMCdlypCp/5qcdwMrd/3Tp5jUuCKykibw+7U/8+uaSa54Ypxnf1idL3TNYMzwBYQ7Y3hr82852Vje/XG6ydjhC7lyyn+ha6aadVdCmqkbfGfdUurcPa+l0YAbcyby1uHdNHhZMnK6g40n2XD8IBMTswb9wk1TN4hxhnN34Qw+VTSTbTVHqGppQNd0hkXGMiourXPdgKIEQtMN0sbfQltjBUc3P+5NBLnzvkN89hz1uTNEDJnkXdM04pzR/HH6D/nS2h/T5MXizeGRGfxpxo8QUoTcLqv+0HWDC0Z/hoNVGzlU3fcOqwBhzhhumvVbbGFxtNm/jj3XDb+svWXi0Lj/8Yuu6eSmzuBLi5ZSWvkJG/e/QHVDGZbtJtwZw4hhFzMl/yYiXHEIKdDVG6gSwmwh2HHyGPvqen9PyIyMIysqnp9vedvva712aCdTk7P9jg81HTchExMzEac6KeinJodU4q4EU/bsrxAel83hjf+irb77nX4jEgvImvEA8TlzVeI+hAyZ5B3au66Mjivg0bm/5WdbH2b7yb3dHmdoOhelzeSHE75EuBGGMYTeUDU0bp/3MM+v/hYlFat7PTY6PIU7LvwLsZEZSOCGnAX8vx2+zdobms4tOYvUhxLtpUsAOclTyUud2eU1IazOuniVuCuhztB1lpT2PQEQ6wwHoLrF990eOxxv9T821OlD5ImuErqSR15J8qhrqCv/hON7Xz/Vx13DFZ1GyujriE4bj7AtlbgPMUMqeYf2mY3syAwem/tb9tcf5Nmy19hTV0qL3UqUGcHM5InckruIRFc8trCHVOIO7bPvErh93sOUVq5lffGzFB/7uEsrw9S4Iqbm38L4nCvRNKMz6bwx+wqWH/2YLTW7vb7eF0bcMSg69Qyk7hav+rqgVVHOtXXH+25F1zGzHEiOqp3Pj+wUJUDaqc+W2MxpxA2f1fnvUsr2rjK0L3JVhpYh+RPt6HueF53Ff43/YpfZj9MT9qGWuEP7brOrj29i7fGtXJw2g8VzH6S+9QR1TccQwiIiLJ7k6Gzq2urQ9a5bc+voPDTjR3z5k5+y9eSePq/1mcLF3Ft4U399KYqinEPetD2sc7f3ck8Nj+Fg40m/rpMSHuVXnKIo/6GdMUGkaZpqBzmEDcnkvUN35QlDMWHvIKTg1UPv8Yttf0EgeKZsGXlRWczPmE28MxaHblJfV8LmXY+zumozc1Kn8Ptp/9W5YYOh64QRxt9n/4KXDr7NCwfepPSMxZc6OnNSp3BH3jVMSxr8m6woitK9MMOkyXL3esyR5lrKGk6wKGuMVzP13bk2ezy2EENmx1VFUZT+pkkZ6N6kSiiwhMXWmj184ZMfYvvQgvDmnEV8b9znuj2fqZtsrdnN7roSWu02YhxRzE2ZSkp4ouqaoChD3Bc+XsKG6kN9HndL3iS+OuYiFr39t14703QnPzqJZ+ff6+8QFUVRzktqqmOIMHWTf+571qfEHeDlg+9wsq2u2/MBjIsfwY3ZC7g99xquzppP8qmdbFXirihDlyVsbsqd6NWxrx/aiSUF94+Y2ffBp9GAz42aM+j32FAURRloKnkfAoQUlDcdY/0J73c37GBJixcOvtXjB6iu6Th0B07DgUM3h0w/fFvYeISFUBslKcpZTN3govRCksL63nm6yXLzx+0fcFv+VG7Pn+rV+TXga2Mv5qL0QjURoCiK4iOVvA8BQgqWlb/nd/zrhz84Lz5ALdG+s9zBxiO8dXQFr5a/y/Kjq6h1t+8eaasZQEXp4keTFmJ4ccP+8sFt/G33x3x93MV8b8Jl3e4w2iEnKoFfT7+W2/KHzuZ4iqIoA2lIL1g9XxiaQVVrjd/xxwOIHQyEFGhofFCxliVlr7G5ZleX103N5OK0Gdyedw3j4keohEJRaO/1PiMlh19Nu4YfbnidNtH7tuofHi3mjoKpXJs9nutzJrCqopQ3yndyoq0JDUiNiOG67PFMTsrCErb6O1MURfGTWrA6BEgp+Z+tf+JVP2ffw40wPl60JMijCg1CCmxp8/2Nv+ODik/6PP7zI27nM0WLB2BkijI42EJwtLmOp0s28Gb5rrM60GRHxXNj7kSuz5mAqRmdO4xaQnT+79PPpbrKKIqiBEbNvA8BtrRJDUvyOz41PDGIowktGprXiTvA3/Y+jUM3uSf/BjUzqCi0z8APi4zj2+Mv5atjL2JVRSm17hZMXSc7KoGJiZnddp86M3HvOJeinI+ksNHO+BuRp9Zcqd1PFV+p5J32mWsh5aD9YDE0g2uGz+f/ip9D4vuDlCszLx6SrR8tYfHesTVeJ+4d/rz7KRYNu4jksASVwCsKdG50F2Y4uDijCPtUKVpHPfxQe+9QlGARtoVumDRUbKNi+xIaK3dge1ownJHEDptK2vjbiEwqRAhL7cSteO28/U3p2Gn1ZFsd207upclqJtKMYELCSOKcMYPq8a6maWREpDIjeQKfHN/iU6xDN7kx+4oh+eFr6ibPHXjd5ziBYMmB1/niyDswGHrfF0UJhK5p6GrnRkXpkxQ2LTUlFL/7Q1pqSrq8ZrXWUlV/hKrdS4lKG0/R5b/EEZmsEnjFK+ftb8m2k3t5tmwZH1asxZL/6TJiaiYXp8/k9tyrGZ8w8hyO0DeWsPlc0W1sqN6BJXtfWHa6W3IWEeMYetuTSyk50HiELTW7/Yp/5dByvjDijvaedoqiKIriAyEsGit3sPvVBxBWa6/HNlZsY9vzdzH2hn/hislQCbzSp8ExtRxkv9/5CJ9e/X3ePba6S+IO7X3Plx/9mHtXfZc/7nrs3AzQD6ZuMCa+iJ9O+gqml7Nil6XP4Wuj7x2SpSG2tNl+cq/f8bXueo63nujyb1JKhGgvSxKi6/9W674VRVGUDnZrPXte/1qfiXsHq+Uku1/9Iqi9RxQvnHe3dw/u/BdPl77q1bFPlryMoel8edTd/Tyq4DA0ncszLiDBGceDux5lX31Zt8fFOaO5NfdqPl14i1818oGyhIWpm5Q2lHO89QSGZpARkUpGRErna4GSSFrttoDO0XIqXkqJpmnUNUrWbfewvdii6dQu8FERGuOLTKaPNYmJ0jqPVRRFUc5PUlgc274Eu63Bp7i2hmNU73uTpKJF6Iajn0anDAXnTfJuS5s9daU8VbrUp7jH9r/I5RkXUBiTjT4IVoTrms7kxDE8c+Ef2XlyHy8cfIvypmNY0ibeGctlGbO5POMCNE1r/78BrAuxhIVHWLxa/h4vHHiT0sbyLq9PTBjFLTmLmJ8+Bw0wAqjD19GJdIQHNN4oM+LUrDq8+mEb24vPLkdqbJas3uJhzVYPE0aYXDXPiZQSXVcJvKIoyvlJo2rXy35FVmx/jpRR1wZ5PMpQc94k7zo6z5Qu8yv22bJl/GjCl4M8ov7TMXM9Mi6fH8V9uctMcLBmtn1lCYvypmM88MlPqGyt7vaYLTW72VKzm1GxS3l45o+JMiP9Xkhr6AYzkiZiaDq2H48hMyPSSA5LQEp4+o1Wyo70fg4pYcsei8Zmya1XuPwas6IoijL41R3diKf5RN8HdqPp+B7aGo7hik4P8qiUoST0p5KDpNFq5r1jq/2KfefoSppt7+rWQomhGWeVcHQk7uLUbomtnkZONBygur6MlrY6AOw+dlL0lSUsqlpP8OnV3+8xcT/d7rr9fGbVf+EWbkQA9X9JYfFckDrNr9g789tnPpZ/4u4zcT/d/kM2H6z3IFQNvKIoynlHChtPU9+fc71x+5n4K+eP82bmfU9dCW7h8Su21Xazv/4AExJGBXlUA09IG10zOHh8E+uLn2XfsZXIzkW7Grkp05hWeCtFGfOQUgRl1bupm/xw84PUur2v/yttLOfBXY/y/XGf9/u6lrC5PfcaPqxY61NcuBHGNVmX4rFg0y7fb2Q27PRw4VQHQ7D7pqIoitIbTQt40yVNtWJV+nBezLxLKWmyWgI6R7OXK8ZDmRA2ltXGEx98lqc++jx7j354WuIOICmrWsdzq77BP965jWZ3XcCz8EIKSuoP+dWy8c3DH9Fqu/s+sAembjAlaSz3FtzkdYyh6fxqyrcxcbBlj4XHjy+/zQ3b9lnYQs2+K4qinE80TScsbngAJ9BxRacFb0DKkHReJO+aphFtRvoVmxOVybfHfIYpSWMBaLFaafA0ddZRW8LuLTxkSCkQ0uKJDz/DweMb+jy+qq6YR9/7FG2eRkSAX+OzB17zK67FbuXVQ+9iBXgD8aVRd/G5otv6XJwbZrj4/bQfMCdlMoahsfeA/9fdU2Zj9NOiVSkFUgo8dhvV9Qc4dnIPJxuPAMEveVIUxTuW+E953ellc6f/u3J+iEoZTXhCvl+xccNn4wiPD/KIlKHmvCmbGR1XQLgRRosPtev35N/AV0bfQ2VLNY/se45XDi2nuu0k0D5DOy91Ootzr2Ra0ngsYYf8LqXLt/6RYye9nwGvbTrCsvU/YfHcP/p9TV3T2XRip9/xm2t2cmveVX0eJ6QAKdF7+Bl8pmgxV2VdzLNlr7Gs/H3qPY2dr2VGpHFD9gJuyF5AhBHW2VWoOYCHLS2t/TfrfqLhIGuLn2bHwTdxW82d/x4flcWU/BuZnHcDDiNMbfShKAPAEgJd01hVcYQXSvex9cRx2mwbl2EwMTGFm/OLmJM2DCEl5iDZtVsJjLAt0sbeRNmK3/gcmzZuMVJYaOr9W+mFJs+T3WWEFPxy2195+dA7Xh3/wMg7ua/wZh7e/SRPlLzUa8eSophcHprxQ+Kdseekk4s3PFYrf3j10i7Jnjc0TecrV75OdHiK3/3LFy6/j6pW/xbgzEqexMMzf9Lta7awMHST8uqt1DUdRSKJiUhjeNJEhBQYZ/wspJQIJEIKKluqabFbiTIjSAtPxpbirJuv/3uphaNV/s2aDU/X+dS1gbWq7M7yLX/gk31P9XqM04zk5tn/j5zUaWobe0XpR7YU7Ko5wX+t+5iqlp7fW9PCI/nljLmMjEvAUAn8eUFYbWx7/k5aakq9jokZNpXR1/5N7RWi9Om8ehe5Pe9qr/qaX5V5MfcV3syvtv2VR/e/0GerwX31Zdy98tvUuhtCsozGFhZbDrzqc+IO7SUa6/c/iwyg60uE6X8SG9lNrC0s2jxNrN33NA+/cS2PvX8vL6/9Aa+s/W+e+ODT/On1q1mz53Fa3PVdykg0TcPQdBy6SWZkGoUxOaRHtN+UdPfUJDHW/z+PxDg96LuuvrHxV30m7gBuq4lnVn6Fg1UbsYWFbXsCLn1SFKUrSwi2VFfxxZXv9pq4A1S0NPGFFe+yvaZaldGcL3SD0df8hbDYLK8Oj0ody8hFD6odVhWvnDfJu67p5ERl9blbqobGZ4pu5bXyD3jh4Ften7+q9QTfXP/LkCydMXSTskrfOq6crrRibY/lKH0RUjIhfqTf1x6fMKpLzbstLOqajvL3t2/hvW1/5OQZGz0B1DUf44Mdf+Zvb91EdX2ZX3XgQkimjvH/Kcq0MWbQ9q61hcXeIx+yseR5r2OEtHhh9bcRwsPRkzvZWf42Le769qcPKpFXlIA1eNx8e80K3F4m423C5ltrPqLZ8q/rmTK46LqJGR7PuJufJGX09ehmWLfHma4Y0ifexZjr/4luutBCMIdQQk9o1nj0E13TuKfgBgxN54+7HkN2k17NSplEZmQa397wa5/Pv6N2HztP7mNkXAFGiO3G2nZajfdAxoLk1tyrWFr+rs+RYYaT64Zf1qU3fXPbSR7/4NM0etEvvrG1mic//Cz3X/okMRFpZ5XR9EbXNbLSDFISNKpqfEvDM5J10pKC9wZs6Cbrip/xOa7V08C2g6+TlzqLxz/4NLpmMjrzUmaOuJPUuKKA25kpykBq3/Oh/clps+VGA8JNJyABDX0ASw1sIXihdB9NPibiDR43L5UVc2fhaFX/fh7QdRPNEUneRf9FzpxvULV7KY1VO7E9zRiOSGIzp5FUdEV7a0hNV+UyitfOq+S9w+1513Jx2iyePfAay8rfo8HT1PnarTlXsf3kXvbVl/l17mfKXuNnk74erKEGjcPo/q7fq9gAyl50TacoNpexcUXsqN3nU+yCjHmEnzZuXTd5c+OvvErcO7S461i2/qfcffE/fbo2gC0kC+a4+PfrrXj7pNvQYcEcJ7aQQek2I6XkZNNhDlSt9yt+w/7nmZJ/E3mpMympWM32Q2+wo/wtFkz6DlPzb1YfFkrIE1Kgazr766t5tmQj7xzZQ5vd/jQt0nSyKGsMi/Mnkx2VgJByYJJ4TWNp2X6/Ql8u3c89RWOCPCAlVGmnbtIMZwSpY28m3bit8zVhW+jGeZmGKQE6L39rdE0jIyKFr42+ly+PvJuDTUdo9DQR5YgkJ2oYf9/7rN/nXlG5LuQSIiFs0uNHUXxspV/xafEjkVL6/XVZwuJnk77O3R9/q8uNUm+yI4fxjTH30TGrBtDQcpx9fnwNB49v5ETDQRKisnyabTZ0jex0nevnu3jlvTbsPhJ404AbL3UxLEVHD1KbSCEtSivW+B1fVVdMU2sNyTF5lFS07zAspeCtTb/GaYQzLufKzu46ihJqbCFwC4vvrXuV1VVnT6g0WW6eL9vM82WbWZA5ih9PXkj7LHz//k7vOXmC463+7R1S0dLEvrqTjIhLCPKolFB3ZqKuEnfFX+ftp3bH4kWn4aAwJodJiWMojMlB1/QubQR91WS19LnAdaDpusGUgpv93rVteuGt3ZYYecvUTTIiUvnn7F+S5Oq7f21BdDb/nP0Lwk5r2yiEzYb9z52xqZT31hU/69dXoOsao3IN7r0+jBE5Bt3dv+g6jMozuP+GMAqzjaAl7tA+897m5Q1PT9o8TbgcUWf9+5ubfoXlQ+tURRlIQgo80uazK5/tNnE/09uHd/O1NS8iJUFfLH46KSUnWgP7u6kJMF5RlPObuu07g5QSp+7wO97UjJCrdweICktkRMaF7Dnyvk9x6fGjSI8fFfD1Td0gNyqTFy/+Cy8fepsXDrzF4eaKLseMii3glpxFLMych4beZfGvrhscqdnh9/WP1uzwezZO1zXSEnUWXxFGY7Ng616LhmaJBsRE6owfYRAZriOEDGriDu0LqAMpWwJwmuG4u9lh2GO3srn0FaYW3OLTegBFGQgaGr/Zupw9dZVex6w7fpC/7l7JF0ZfgOFFZzG/xqVpOI3A3uNdhlqUqCiK/9Qn9hkkkB/t/9bGeQHE9ichLBZM+jblJ7bS5GXPdYcZztXTftLZTz1Qpm4SpZvclns1d+Vfz+7aEqpaT2BoOpkRaeREZ2IJq8de+Z5uElBv+dMm83QdSXlUhM7M8Y6OSh6kBNPQuhwTTLpukJk4zu/4mIg0IsMSqGs62u3rG0ueZ0bR7X6fX1H6S72nlbcP7/E57qUDW/nsyDkYASbYvcmOjvU7VgOyoqKDNxhFUc47oTdFfI45dJOFmRcSZUb4FX9j9oIurQ1Dha6bRIYlcvdF/yQmPLXP48OcMdwx7y8kx+QGfVa2IzkfFZfPvNRpzE2dSk50ZpfXuuM0I/2+pss8u2zEX4ahYejt/9eRuPcXTdPJSBhDWtwIv+Kn5N9Ei7uOvUc/6vb1Ew0HsWx3IENUlKCzhODFsi14/Ghr2uBp463Du/2K9WZcAOkRkUxJ7vt9tDszUtNJDvfv80VRFAVU8t4tp+5gUeZFPsdFmRFclXVJyO6yaugm8VGZfG7Bc1w09ovERKSddUyEK47ZI+/hCwteICNhDHo/fy3eLoK1hUVOylS/r5OTMnXQ9je3hcWUglt8jjN0B5Pyrud4fRnzxnyWOSPvpSjjwrPWPlh2W7CGqihBYeo6H1d4vzPlmVZU7McRpH7ZthBIKWlwu3lm/x6+ufpDDjbUc2NuoV/nuzmvSG3UpChKQEIzywwBnxtxGysq11PRctzrmK+Pvi+gevmBYOgmhjOa2SM/xdxR91FWtZ66pqNIKYkKTyI/bTYaGlqI9Zw1dJPJ+Tfy0c6/YwvfZoo1TWda4a2Dtq+5oZtMzL2WPYff6+wY440rJn2XcGcs8RFjiUwfg8uhER3ppL75BJtKn2NT6Us0tZ4IuKZeUfpDvcf/MrkGd3AWhFpC0GJZ/GHbBt49fLBzQ6Z4VxjfmzSdKcmpbDzufU3+jJR0ZqcNG9Ce9IqiDD0qee+GrulEmRH8fdbP+fyaH3KsparX4zU0vjr6Hq7LvqzX46SUSCS6ptNitSKRhHX2MZcD2rKvoxQmN2U64lQHF13TQzrBDXNEMybrcrYdfM2nuML0C4gOT+6nUQ0MDbhlzu95ftW32F+xqvdjNZ3LJnyTyfk38MbKNjbs/E8ZV1KcxZQxMcwc8RlmFN3Nip1/UYtVlZDkDOD30hmEFny2ENS72/jsiuWUNzZ0ee31g6XMSx/Gb2fO4+urPmRbTd+TPJOSUvjNzHkBj0tRFEWT/dlTa5CzhEWT1cLj+19iafm71Lrru7yuozM7ZRJ35V/P5MQxPSbfHT3SjzRV8kzZMl47/H5nv/Mww8nlGRdwW+7VFMXmdm5IopxNCBuP3cq/3r2b6gbvNtGKjUjn05f9mzBHNPog33ZaSIEG7Cpfzvr9z1FevbnL66bhYnTW5UwvvJ2U2EJe+8jD1r3dr79wOuDGSx3kZ5lIJMYg/94oQ89/b3iNtw/v9iv29vypfHXshQG9l3qE4N4P3qS4rrbb112GwW9nzmNSUipP7dvFKweKqWo5+2lBangE1+cWclfRaDSNkOxGpijK4KKS9z5IKRG01zy+f2wNB5uO4hYe4pwxXJo+m9TwJCxhd2lreDohBUJKfr71YV47/EGv/dJnp0zmN1O+i1N39Hi+850QFq2eRp5e8SWOndzV67HJMfncceFfiHDFD6nZ5Y7uP9X1B6g4uRuP3UqYM4bclBm4HJHsPeBh9Rabw5W919VqGiy+wkVBVnB70ytKoGwh2HHyGJ9e+bRf8S9f9hmGRcQGsLGc4J3DB/jpht43SDM0jc+MGs+NeYVEmA5WHjvMthPHabYsIkyTycmpzEkbhpASU1dJu6IowaGSdx9YwkLQ3t9bAg7N7PPDwZaCr6/7OauqNnp1jZGxeTwy59c4dYeage+BEDZosO/ICtbvX8KBqnVdXs9Kmsi0gsWMypyPhCGVuJ9JCBuJRNN0mlok/3q5jboG7/+kHSZ87c4IwsNU8q6Enlvfe5SShmqfYqYlDecvcxcHfO37PniLnSe9a6vrMgwuz8zm2pwCsqNjiHI4sWX7Z4VK2hVFCTaVvPcjW9j8c98S/lm8xKe4RZkX8bNJX++nUQ0dHTPQjS3V1LdUAZKosGRiIlKC1pt+sJBS8uqH7h7LZHpzyXQHsyY6MNTsuxJCLGGzveYoD6x+3uu2j6PiUvnTrJuJcriQQFlDHU0eDxGmyfCoGJynNkfqa8Ho4cYGbnznVb/HvuSyq8gJoBe8oihKb86f7OYcEEieP/iGz3HvHPmYb4y+j3iXevPvTUdyHhWeRFR4UrevnS/cHti537/9BTbutpgzKbS7JCnnH1M3GJ84jF9Nu4YfrF9GWx/7Z1w1fCz/PXEB1a0tPFm8lWUHSqh1/6cNaoRpckVWLrcWjGB4VEyvT02PNjcGNPajTY0qeVcUpd+o53n9xCMslh9dSa27oe+Dz2BJi+cPvIk1SPuSKwNLSknZERvLz1+XugZJTb16AKeEHkPTmZuax+MX3cXCrNE4u1kLFG44+P6Ey/nRpCt4/VAp17+9lCf37eqSuAM0WxYvlRVzy/LX+Ofu7b1eVwT4QFr9NSmK0p/Or+nJAeTQTT6qWNf3gT1YUbmez464NYgjUoYqIaG1LbB0IdB4Rekvhq6TE53ATycv4tvj5/PO4T1UtzahaZAaHs2CYaNwGiavHSzlF5vWenXOR/ZsR0jJ58dM6Pb1lAB3QE0JUzuoKorSf1Ty3o/qPP4/eq33+D5jr5yfdA0cZmD16s4A4xWlP3W0V4x2hHFN9jigfXZbAxy6wfGWZn612bvEvcOje3cwN30Yo+ISMM5YVJoXE0dudCxlDXU+jzUnOobCuHif4xRFUbylymb6kSuA3VadujOII1GGMk3TSE/2/0/ZYUJczLlL3oWwsYWFWjuveMOhGzh0A+ep/28LwfMl+7D9+P1Zsn9Pt4tXLSG4Ka/Ir/HdkFuIJXpv06ooihKIQZG8W6d9sNtSDIpacFsKcqOH+x2fF50VxNEoQ11CrE5Ohn9/zuMKTcwB3lbAPrX48PCJ7ewsf5sdB9+gpGI1trDa21+qRF7xkgRePbjfr9gPjpZT73Gf9e+mrnNVdh7pEZE+nS8lPIJrcgpUe0hFUfpVyJbN2MLG0A1OttXx8qF3ONh4BLewiHVGc0naTKYnT8ASFmaIdhXR0bglZyFPlbzS68ZMPbklZ1Hn90A5dzpmhDWtfWGooTv83vilP9lCMm2sgwNH2/o++Awzxg1cp5n25Nxic9nLbNz/wlk75Ua44piQcy0zim4jwpVw3nUNUnxX3tjAyTbff++hfRfVzdVVXJieedbftanrPDTnEj63Yjk1ba19nivO6eJPcy/BoRJ3RVH6WUh+MlrCoqKlmod3P8EHFWuxZNcWYc8feIPhkRnckXctN+VccSq5Cq2EStM0MiJSmZE8gU+Ob/EpNidqGFOTxvXPwJQ+SSmRUiAR7C5/l+qGA9jCTbgzjlGZlxIfNSzk+sgbusbIXIMROQZ7D3j/ZGrmeJPkhIFJNmxh0dxWw1MffYHq+rJuj2luq2XN3sfZVPoiN8/+PdnJk9HVDazSiyaPJ+B4ISVGN8l7RmQUj158Bf+zYQ0bqyt7PMekpBR+PGUWyeERatZdUZR+F3KbNFnCYl99GQ988hPqvVjwed3wy/jhhC8NwMh8ZwmLQ03HuPfj79BoNXsVY2oGf5rxYyYnjgnZpwr+6C7ZFcIekMSs49pCCiy7DdNwoWt6t2OyhYXbamLV7sfYUraUFnftGWfTyE2ZxqyR95CXOjOkbhqllNgCXninjX0H+07gp401WTjXNQAjAyEFbZ5GHnn3Lk42lnsVYxou7r7oH6TFjwqpG6XByJYSHWi1bU60tmBJQazTRbwrDEuIQZFwnjnOjv8uqavl9vde9/u8v55xARdlZPX4t9xxnUMN9Swp2cvWE8dptjxEmA7GJyaxOH8E2dGxg+b7qCjK4BdSybuQgsqWau5c+Q2f+qPfW3ATD4y8M6QSqQ6WsNlZu4+vrvsZDZ6mXo916g5+NukbXJI+E10bGh8CUgrcVjObSl9ma9mr1DYfRUpBVFgiY7IWMK3wVqLDk5FSoAX5axbCQgI7D73NhpLnOXJiOx09KjITxzO14BZGZ12GBui6iS0s6psreeqjz1HbdLTP81809gtcMPozXf7NtiWGoXGyXlBTJxASoiM00pIMbCH7fRfTjj/nbfssNuy0OFJ19sK5/EyD6eNMCrMHLiGWUvDGxl+xqfRFn+ISoobzwKJX+mdQ54GOp5K7T55gScle3jt8EPdpiynHJyRzU34R84e1r88JteSzo9+627Z541Ap66oqaLI8hBkmo+ITuLVgJKams/CNF2n0YwZeR+PVhdeR7EVryI6xnL7Atbt/UxRF6W8hlrxLfr71YZaWv+tTnKmZvH35o8Q5Y/ppZIGxhE1lazWPFr/AW0dW0GJ3rZ80NZNL0mdxb8GN5McMx9CGTpnARzv+xuq9T2DZ3deMaprOuOELuXLqD9E1I2gz8ULaHKzayEuffI/mttoej4t0JXDDrF8zPHkSbk8L/3hnMXXNx7y+zoJJ32FawS1I2f7hvavEZv1OD+UVXZPmhFiNqWMcTB5lYhj0exLfcRNRecKmvELg8YDTCflZBnHR+oDcSJzObTXzh1cvw2O1+Bx754V/O1U+o2bffSGkxJaSn6xfzbtHDvZ6bEZEFH+aewlpEZEhk8DbQmBJycM7NvP6wRKarLN3WE1whfHqwuv40/bNLCnZ6/M1Lkgfxu9mXRSE0SqKogyckErem60WLnvnblrts1f/9+ULI27nUwU3YYZofayQ7clcq+3mnSMrqWg5jo0gyRXPFcPmEeuMxhbirH7Dg9lrG37O5tKXvDp2eNJk7rzor+iaEfAMvBAWpZVrWfLx1xGy9y3VoX3W/da5/0tW0gT+9tbNPiXvDiOMb1zzLhDOE6+2cvT42TPdcdEaU0abTBrpICK8a8IshETvxyRaSokQ/+mJresM+BMqW3jYWPIib2/+rV/xI4ZdzC1zfh/kUQ19Qkq+ufpDVlf2/RQJ2hdcPnbxFUGr2+7u/czb0hJbStpsiy+ufJfdJ2t6PfZHU2YxNiGJxcuX+dwa4E9zL2FKUuqQet9VFGXoC5mpLI+weP3wB34l7gAvH1rOp4sWB3lUwdNRBhNhhnFl1sWcvoG241Q/+KHyAWILi82lL3mduAMcqt7Em5t+zZVT/jvg6ze763hhzXe8StyhPdl/YfW3+eLCl5leeBvLt/7B62t57FY2l73CmMybqTjRNXHXdVg418nkUSbNrbBpt4c9B2xaWiWGDolxOlNGm+RnGQjZP7PxmqZhnOP7WV0zOVaz0+/4YzW7gjia84MlBP8u3u114g5Q627je2tX8vglCwO+tqnrbKyu5OWy/RxubMCSgnhnGJcMG85V2Xk4DaPXUhND0/j2mo/6TNwBnivZyxXDc/j8mAn8dedWr8d5XU4B01PSvT5eURQlVIRM8g5wpKnn1fx9qWg5ji1F5058ocwxxB//65rBmr1P+hy37cBrXDLuK0S4Yv2+thA264uf9bk8o70u/yWmF97GBzv+0mOZT3c2lbzIjKLbycmwKD3cnsAbOty60MXwdIPXVrjZttfCPmNSvrrWZu8Bm7hojSvmOCnMNkJy3UagNE3D7Ue5TAe3l4u9lf/QNY0XS/f5HLentoadNdWMjE88q/uKN4SUrK48ykPbNlHedOa6pTo2Vlfy552buSG3kC+OnYSGPGt9jyUEW05UseG4d58He2pr+P3WDXxn4nSElPx917Y+Y67LKeC7k6Z7+2UpiqKElJDJdDXAkoFtvtRRmqKcO7awKKlYQ23TET9iPWwqeaFzAx9/bS57xb+40pdwmRGMHb7Ap7jqhgPti3Aj/vPndPVFLoanGTz1Wiubd5+duJ+utkGy5O02tu6zOhfADSVSSlyOKL/jA4k9H1lCsKriCJUt/t30PFey168PBiElrx7Yz3fXrOgmcf+PZsviqeLdfHvNh0jJWRtymbrOkv2+1a+/WFrM77as51MjxvL4JQtZNDwX5xlPMg1N48KMTP58wXy+P3mGWmSqKMqgFTJTwAJJfAAzrpFm+JCf0R4MDN1kV/k7fsfvPvwuc0ff73f80ZqdNLWe8Cu2oeU4R2t2kZEwli1lS32IlNjC7tylNCNFZ3yRyQvLW89auNrjGSS89pGbxFidjBR9QBeT9jchLXJTp7P1wKt+xeekTu+XbkRDlQZ8Uun9uo0zrauq8PkJkCUEW08c57db1iO8rDxfVXGU323dcNYMeIPHzaoK32/+ny/dR3HdSX414wJ+NGUW35gwla3Vx2n0uIkwHYxJSCQxLBxLqEkeRVEGt5D5NHRoJldkzEPDv6Tlsoy5Q3LWcjBqbjvpd2xTALHt8X3XyPamxV1HmCPapxjTCMM0HLS623//po4xqT4p2FXi25MkIeDjTZ4hlbgDGLqD0VmXE+GK8yt+RuFtwR3QEGfoekAbFzV6fF93ZOo6j+zeju3je/DSA/s5ecbupdUtLT6fp8OWE8dZVXEUW0qiHU7mpg/jsqxsLkgfRmJYeOdYFUVRBrOQeRfTNI1hkalMTxrvV/xtuVeDz70GlP4QSEu/QDfjcRhhAcWbhgtL+LbV+oiMeQAcOy4Id8HYfJMNu/xLnvaX29Q3irNKCQY7DY1JeTf4HDcsYRypcUVq1t0HQkrCTP9XKYeZvv0NCikpb2zodQfSnthS8kLpvi6z4YFOwpyZ+BuaPiTXkiiKcv4KqU9ES9h8quAmn2ffZyZPpCAme8hsbDSYCWmTEJXld3x8ALEASTG54OfTG03TSYge7nPZzZT829h/yM3JesnwdAPT1NhR7F/dvpTtGywNtSf7mqZz4ZjPk5U0yeuYCFc8N876dcBrIM43thSMiEvwO37kqViPbWN78YsopOS1gyV+X++tQ2VdZsMTwgK7AU8KC/drsa2iKMpgEVLZrqkbTE0axzfG3Od1TF5UFr+Z8l3sABe7KsGhoTMl/ya/4yfn3RBQshYTkUpe6ky/YvPTZhMbkcb2g296HZMeP4rslAls2Nme5IS5NISQNHvfrOYsjc2SoZZ7aJqGhsYd8/5MftrsPo+Pi8zg3vmPERWeHPDTmPONQzdYNDyXSNPhV/xNeUWcaG3hpbJi3i4/QE1r+y9zT4m8oWlUt/rfTej4GbHxrjCmJKX6da44p4uZqelqpl1RlCEt5D4VdU3jttyriXNG8/ud/6LWXd/tcRoa81Kn8bPJX8elu4bUrqSDmaZpxEdlkpMynQNV63yKjQpLYuSwSwLaZdUWFtMKF1Naucbn2GkFt1LTUE5VXbFXx0eHJ7N47oMcPe6m+FD7zaOUBJx4a/rQLADT9fZWmLfPe5gDletZv38Je49+hDztxjsjYSzTCm5hzPAFgKYSdz85dIOFw3N5wcd2kSnh4cxNH8avNq1l2cFSoD05n5eeyW2FIxmfkHxWYtyxAZi/zoy1hOCm/CK/ynCuzskPaCyKoiiDQUh+MmqaxoJh87g84wKWH13Fiwff4kDjETzCQ6wzmovSZnBr7lVkRKQOmt7u5xNbWMwf/xUe/+B+LNv7+vH5478S4HUlhm5SmH4BhekXUHxspdexRRnzyE+bhaZpXDT2C6zY9U9EL08AUuOKuO2Ch4hwJbBum01HmW1Lq0TTNGIiNeqb/EvBY6OGaPYOnbXrw5Mnk5M6jRZ3PQ0tVQhhEeFKICYiBVtYKmkPgs+PnsDaymO9tm08naFp/NfkmdS1uXnn8MHOf7el5IOj5XxwtJzPjR7PfSPHdYkTUpISHun3OFPCI7r8t6nrXJiRRV5MLKX1dV6fJ9rh5Nb8kaoFpKIoQ54mQ3xlnCUszDM+yG0p0NHUo9EQJoRNScUaXljzHS82PNK4dMJXmVl0l98/UyEkR48LPlrvYcIInaJcyXOrvkZZ5do+Y/PTZnPLnD9g6CaapiOlpMVdx8aSF9hU+hL1zRVA+06hRRnzmFZ4KzkpU7GFha4ZeCz418stVNVIHCZ8/a4I1mz1sHKT74tWDR2+fncEEWHqd1sJjCUEJ9ta+fLH71HW0P0TzA4u3eCn02YzO20YX1r5Httqjvd47JfHTuL2wlGdSbKUkoqWJq5/a6lf95xfHDOROwpHdal7t4Sgtq2V+z98h4qWpj7PEW6Y/O+cixmTkKS6ySiKMuSFfPKuDF5C2FTVl/DRjr9SfOzjLuURHTITJzB39P0UpM0JKHHfc8Dm5XfbsAUYBtww30FRjsH6/c+ycf9z1DSWnxWXGJ3NlPybmVa4GA3trI4mQljouollu7GFB6cZjqbpCGF3Ke0RQtLqhqdfb+XoccEVc5yMzDX433+34Otf15gCgxsvDWzBnqJ0sITAIwQvlRXzcmnxWbPwEabJFVm53JI/gpTwCL6/dgVrqyp6PaehaSxbeH1n68UOX/n4fdZW+dZf3qHrvLHoRmKczm7H3uBx8z8b1rCm8miPNwZFsfH8aMos8mJiMVTirijKeUAl70q/6kiAG1qOs6XsFWqbjiGlIDIsgfHZV5Icmx9QmYQtJEcqBU8sa+3SoUXTYPZEB9PHaURHOCg5tpbDJzbTZjXhMiMZnjyF3NRpQSvREKL9z2hniU3xIYsb5ofx5sdtrN/h/eJbhwmfvjGcxFgNfYj1elfOLUsITF1n8/FKDjY2YElBvNPFrLQMHLrO+0fKeXTPDsoavCtTuW/kWD49clxnsmwJwZ7aGj730XIsH3a6vqNwFF8eO6nHG3dbCAxd51hTI8+V7GX98QoaPR7CDJOR8Qnckj+C0fGJnV+foijK+UAl78qAsYUHXTMADSFtNE0PSnvPR15q4UhV9wmDrsOIHIPJo3QS4iQuh06bW1DfqDE83RH00ivblhiGRmubxOmEF5e3sbu0705IpgmLF7jIHWaoxF3pN1JKbCmRSFosi6eKd7PsQAk1bb61R0oMC2PZwhu6tGS0peD9w4f48YbVXm2ydOmwbH4+3bsnblJKBHS5Xnf/piiKcj5QybsyaEkpqaqR/P15/9rU3brQRX6W0W87mnb8aa3a7GHdDovG5u7/1HIydC6b5SQ1UVeJuzIgPELw6oH9/HbLer/P8caiG84qnbGlZEt1FQ9t38Se2u53O453uVicP5J7R45FSKkWmCqKovhItXRQBrWNfu5kCrBpl0VRdv/9CXTMKM6e6GD2RAe7y2z2lFo0t0pMQyMhTmPaGAcJsTq2kCpxVwaQpNUObG+M7uINTWNCYjKPX7KQPSdreKlsH4cbG7GkIM7lYv6wbOYPG97ZzlEl7oqiKL5TybsyaGmaxsk6/x8cnawfmG1MO5LykTkGY/L/8ycnpOxMYvpr9l9RuqOhEeXwbxOnDpFm9x8fHbXnRXHxfH/SjC5lMao2XVEUJXDqXXSIkVIgTy0YE6d1dxmqW8yLAKq+7IHJ3TsZRtcEXddUu1Pl3DA0jVmp6eh+bmlUEBNHnKv3rkjd/X6rxF1RFCVwauZ9iBBSoGs6JxoOsa74GXaVv0OLuw5N04mNSGdi7rVMyb+ZcGfMkEoYczIMoiN1TANa3ZIjVYK6Bu8S+sjwofN9UBRfaJpGSngks9LSWVVx1Of4G/MK1Sy6oijKOaIWrA4BQlhYws1La/6L4mMrejxO101mFd3FJeO/jJRy0CfxHW0epRTYwsY0HEgpKT7oYcNOwf7y3mt6L5/tZNpYU5WsKOclSwg2VVfy5Y/f9ykuxuHktUXX4zKCO/fjEQLz1Gy9kBIhpbo5UBRF6YZK3gc5IWws4eax9++lsnafVzETc6/l6mk/7ueR9R8pBW6rmU2lL7G59GVONBwCJA4znJEZFzOl4FayksZy6JibJW97aOmmC57DhG/cE4HLoRJ35fz25x2beWLfLq+ONTWdP829hPGJyUFJrDvK3tpsm9cPlrKn9gSttk2Uw8GMlHTmZWQhBzCJ7+grfzr1hEFRlFCjkvch4IXV32H34Xd9ilk4+XtMyrshKBsUDbTVe57go51/xbLbejxmWOJ4bp71B1rbonhs6dkJ/IzxJpfPcg76pw+hwBYWumbQ2FqN22rCYYQTFZ4EUqIPwt+v89E/d2/jkd3be9zFFCDa4eS3M+cxISklKL3VLSFotjz8ZedW3jpURot99rqc5LBwbsgr5J6iMWia1i/daTraVda0tvJC6V7eOXyQmtZWDE0jIzKKq7PzuSo7D6dhqO44iqKEBJW8D2JSShpaqnjo9Ss7F6l6KyEqiwcWLe2nkfWfD7b/mY93P+LVsbER6XzqkieorYvm8Vc9dPym52cZ3LbQhaahkvcACGGfegLyIhtLXqS26Ujna9HhyUzKu4Gp+TcT7ooLymZcSv+RUlLZ0sxzJXtZdrCEere787Xc6FhuzCvkqux8HLoelFloSwhOtLbwhZXvcqSpsc/jZ6Sk8ftZF2HoelATaFsKhITfbF7LG4fKetxcKtI0+dSIsdw9YkzQrq0oiuIvlbwPYkLYfLDjYVbvedyv+Dsu/Cs5yVMGxeyoLSz2H/uY51Z9w6e4YYnjuW/+Yzz9Riulh20mjTRZONcJoPqqB0BKwe7D77N03Y+w7J5359R1k4WTvsfk/BsGcHSKP9p3LJVICdWtLbTZFlEOJ4lh4UEvHWmxLO5+/w0ONTZ4HXNxRha/njkvaGOQUmJJyVc+fo9N1VVexdxaMJKvj58StDEoiqL4I/SzNqVHum6w5/AHfsfvPvwuuSnTgjii/mPoJmv2PuFz3JET2yiv3sHls0cQ7nIRGa4NicW6wdCx4LeDkDYaep/fGyFtdpe/y0uf/Bf0WmjRvpj69Y0/xxYephbcjKZm4EOWpmkYaKBBWkRkl9eCmbhbQvBC6T6fEneAD46Ws7OmmpHxiUEp29E0jf+3ea3XiTvAs/v3UBATx8LhuUOiDr7jpuxEaws1be2lQsnhEUQ7nKrWX1FCmEreB7kWd53/sW21gyKZklJQ3XCA8uotfsVv3P8s1874WWdSej4n7h3lVR6rlc1lr1BasYZWTwMOI4zU+BFMK1hMXGTGWYn96ZpbT7J03Y/pK3E/3dtb/h85qdNIis4ZFL9zSv8xNI2Xy4r9in2uZC8/mTo7KOOobWvj9YNlPsc9sW8nV+fkB2UM54ol2t8H3jtyiBdL97H1xPHO1wxNY276MG7OG8G0lLTONQGKooQOlbwPcobu/y6Jhu4M4kj6T8dMr7/2HHmf67SfB3FEg5MQNkJavLPlD2w7sAzPGeUuZVXr+GTvU+SlzmThlO8RG5F+VgIvhM36/UuwhRtfSClYt+8ZFk35fsBfhzJ4SSnZVF3lVZ17d94/cojvTZpBeA+7u3rLEoKXyvZh+bhWCOBQYwMbj1cwMTHlrM40g4ElBNWtLXz54/e6ffphS8lHRw/z0dHDTE9J47czL8Sp64Pya1WUoUr9NQ5yidHZAcUOhp1XNbSAnjB47FZs4QniiAYfIQWWaOOx9+9nY8nzZyXu/yEprVzDI+/eRVVdcbe/H5vLXvFrDNsPvYGnlw5BytBnS0m5j+Uyp3OfWugaKFPXeffwIb/j3yk/OChnoy0hqHW38ekP3/aqbGldVQVf+vg9bClRy+MUJXSo5H0Qs4XF5Lwb/YrVNIPJ+TcOilaREhnQEwbQ0DUjaOMJBbbo+kEqT21q0xNd03lu1Tc5dtK7ft6t7nqeXvElWtx1XT60j9eX0NR6wq8xe6wWDh3f7FesMjRI2ju8BMKf2fLunGzreaF1X2raWgdl+Z2p6/xo/SqO+3ADtKOmmr/t2kpwvuuKogSDSt4HMUM3GZV1KRGueJ9jizLmERWW2A+j6h+J0Tl+xyZEZQ2ZOmshJEJI9pbZPP9OK48vbeHJZS28tsJNRXX7x6ttyzNibMqrt1BWudanazW3nWTtvqe6tCFtdfs/awrQ6qlHBCn58kXHjp0tlsXG4xV8eLSc9VUV1LvbkFIGnFAq3jE0jXhXWEDniHO6gjIWM4BuU4NxIaeQkoMNdWw8Xulz7KsHSrCF+htRlFAR+tOuSq80NC6b8A2Wrvuh1zEuRxTzx30ZIaxB0SbS0B2Mzb6C5Vv/QJvH91rZyfk39LoAczDo6JCzdpvFmm0eGpvPmGU/Iti82yI9SefSWU6yM/7TD1vXDdYVP+vXdbeULeWisQ90/rfDDPf7awBwmhED3vNdSMmhxnqe3b/3rM2AHLrOxRnDubVgBGMSkoJ2TVtKhBBIQNPadyYdjDO1waZrGnPSMog0HTRZvpeyTUxMJi7A5L9DRkQ0VS3+leAMi4zqdjfWULekZK9fcQ0eN2+XHxgyXXYUZbBTf4WDnK4bjMtexGUTvwn0nRyEOaK57YKHiIvKHBSJewdDdzIu+0qf40zDxeRBupPs6TRN45X321j+ifvsxP00x6oF/369lZ37bcSp0hq31cKeI+/7dd3mtlqKj65ECBtoXydhGv7NfGqaTlrcSL9iA/HM/j3cuvw1Xi4rPmsXT48QvHP4APd9+DYPbt0QcF1vRxePLdVVvFC6j6f372bpgRKqWpu7vH4+c+gGC4fn+hV7U15RUL6HlhBcm+tfxxgNuD63cNDVvOuaxvqqCr/j11VVqMRdUULE4M5oFKA9sZtReBspsQWs3v0oZVXrzjrG0J2MybqMeWM+R0xE2qBLZjVg3ujPsPfIBzS0eN+X+cIxX8BpRvZ9YAiTUvLheg/b9nm3uFgIWPpBG7FRYQxL1Wlpq0UEsDC5vqXiVOmMgdOMYHTW5Ww7sMzn8xSkzSEmIsXvcfhKSMmS/Xt4aPsmr45/tmQvAvjmhKk+X8sWAltKni/dx8ulxZQ3dS0v+v2WDcxMTef2wlFMTU4972fhby8cxWsHS2i1ba9jsiKjuXjY8KAkkKauc1lmDn/ctok6t2+LqKelpDEsMirgMQykjnaP/jzt6NBk+dZhSlGU/jO4MjilR5qmk508hbzUGZxsPMyOQ2/S2FqDoZvERWYwPvtqXI5IJGJQLt7UNJ0wZwx3Xfg3nvzo814l8HNH3c/skXcPwOj6l9sDa7b59qErBHy0wc1dV4cH/IRFStn5UEcimV54m1/J+7TCxQNWqiWlpLKliYe2+7ZA9rmSvVyckcWExGSvSyJsIWiyLL666n12nex+Ma9AsrryKKsrj3L/yLF8dvQEn8Y1lOiaRlp4BL+YPpfvfrLSqwWoCa4wHpp7SZDHAV8YM4Ffbz57sqMnLt3gi2MmDqoNjNpvLAVOwyTM8P9vL5BYRVGCa3C8+yhe6ZhNj4scxuwRn2LBxG8xf/xXmZJ/E2HOKDRtcHddMXSTuKhMPn3Zv5lasBiXo/vZr6ykidwy5w9cPO6Bbl8fTGwh2bzbwvJj4rzsiKCmThDhikcL4OceHZ6CduqtQtd00uNHMr3wNp/OMW74IvLTZg9YqZYAntu/F+HDRlIdnivZ61Mtsy1lr4n7mR7Zs4Mn9u7stTvQUGfoOrPSMvjT3EtIj+j9ydjY+EQeu/gKUsIjgpowG5rO9bmFfHbUeK+Od+kGv5wxl6LY+EGTuAspsaXk+2s/BmBUnP9NCkbFJ6qyL0UJEZpUzVuVQUbK9pTMFm52HHyLEw0HsISbCGcco7IuJTkmb9AvUD3d359vofKEfx+acyY5uGiagxdXf5O9Rz/0OT7MEc3Xr1mOaXTd0EtKyZubfs3Gkuf7PMeYrMu5bsbP0TRjwMpFPLbNojdfot7t+6N+Q9N4beENJIT1vTDSEoIlJXu9Ls05/RqvL7oh4M4rg50lBIamsbryKC+U7GNPbQ2ttkWUw8H0lHQW54+gKC6h32e63z9yiMf27mBv7cmzXtPRmJmazudGT6AwNm5QLVKVUvKrzWtZeqCEP865GJdu8IWVvm9459R13lh0I9HOwbGxn6IMdUMju1HOK5qmowG6Ecb4nI5FrBpIia63zzAPlcQdoK7B/9mu+kaBoWtMK7zVr+R9Qu41nd/T02maxqIp36cw4wLW7XuG0spP4IxZ7uHJk5lWsJjRWZd1dssZKOVNDX4l7tA+k76t5jgXpmf2OWZT13m5tNiva7xYuo97R4wdVMlgsHUk5DNS0pmTNqzLa1LKzt+o/p7pnpeeySXDhrP75AneOlRGTVsbhqaRERnJtTkFpEZEtt9oDLKfVYPHw5uHygB4sXQfv5t1EXkxsZTW+7bp3fxh2UQ5AtlrQ1GUYBo6GY5yXgps86bBIZBHYx2xuanTyUycwOETW72ODXNEM7Pozs6Sme7kpc6kMH0utU3HOFC1njZPI04zgqykiSTF5HTu0DrQCzQbPYHtqNvs8SCkxOhl3LYUbD5eddbiVG8tPbCf+0eO83eIQ0p3ybmmaV70zwru9YviEiiKje9M0jueDPQ0xlBmCcHLZcW4T5W6rDp2lPLGev578ky+uPJdrxcLp4VH8tXxk2lfsq4oSigYXO9GinIeio7wP4WJjtBObexkc9sFD5Eck+dVnMMMZ/HcPxIZlthr4t3xhCM2Io1x2YuYmn8z43OuIjE6u8vrAy3QWcJIh6PPVoBCSPbWnV1m4a2qlpaAbzKU4DI0rcvsuqkP3v78pq6z4tjhzv8WSL7zyQqyo2P4w+yLvPobyYqM5m/zLiXa4ez1RlZRlIGlkndFCWG2kIwr9D8Bnjii/QNa1w0cZjj3zn+CMVkL0LWez5mRMJZ7L3mUzMRxXiffmqZh6CaG4cDQzXOe8GRFxRDr506chqYxITHZq6+hzYdWh91xi8DildBw+kLOjoXIHbv6nkv1Z7TBLK2v48sfv09udBz/nn8ldxeN7nbH2szIKL4ydhJPzF9IcpAXCiuKEjhVNqMo55gQNkIKNE1DSomu6afV7mtMGePgo40efG30kJmqk5zwnw9dQzfR0Lhh1q9oaq1hY8kLlFSsptXTgMMIJzWuiOmFt5IaV4Q9SHbf7YmuwdXZeTxVvNvn2AszsrzexTM6wBn+CHPol30NZVJKGjxuXiwtZtnBEo41NSGQxDpdXJaZzeL8EQyPjunssz7QnN2sV9l18gT3ffgWdxeN4d6RY/nMqPGsP15BTWsruqaRHhHJ5ORU6tra1O+nooQo1W1GUc6Rjo44ZZXrOHh8I26rGacZQXbyFHJTp3e+LqXkrVVu1u/wrV/k7Ytc5A4zMIzuk4Yze67LU/22NW3wz7JJKalqaea6t5b63C7y7/MuY1xCUp+LE6WUVLQ0cf1bS/1alzA+IZl/XnS5H5EDy3Oq7lvXNGzR/t0832diOxZg/2XnFp4u3o2nlzvrC9KH8bNpc3Ho+oB/3/5r7UreO3Kox9cjTQeLhucyNSWNGIcTW0pq3W18eKSc5PBwvjJu8qDbSVZRzgcqeVeUASalQErJuv3PsnH/c9Q0lp91TEJUFlPyb2Z64W1omo6U8Nzbbew76F2ZxYI5TqaPPfflK+eSONXR5XdbN3gdc3vBSL46fopP1/nKx++ztuqYr8Pjf6bNYX6QdgwNto5yjxbL4rWDJZTW19Fm28Q4nVyQnsm0lLRBtVFRf/jFpk949UCJV8eOiIvnH/Mux2no6AN0c2wLwZYTVXxx5Xt+xT9/+dVkRkar5F1RQpBK3hVlAEkpsIWHJR9//VR7xd7lpc5k8dwH0TUHoPH+Og8bdnpw97DOMS5aY/5MJ6PzBq6neqh7bv8eHty2qc8Z+DsLR/PlcZN8OrclBBuOV/DVVR/4FJccFs4rV1wXksmvJQS17jb+tnML7xw+2G1df1ZUNLfmj+Sm/KIBbwN6rllCsPTAfn67Zb1PcZcOy+YXM+b206h6dss7yzjYWO9TzOSkFP4677J+GpGiKIFSybuiDCApJUs+/jrFx1Z4HVOYfgGL5/6xsybesmHLXotdJRZNLRJD14iL1pg82qQgy0DI9lp5pZ2QkqNNjSwp2csbh0q7dHhx6QaXZmazuGAEI+IS/L7GX3Zs4fF9O706Ntww+ceFl5EXExdyybslBIebGnhg5XtUt7b0efwVWTn8ZOrs8yp5B7jx7aUcbmr0KUZH49WF15EUFj5g3y9LCDZVV/K1VR9ge/lRH2YY/PPCy0Py91NRlHYqeVeUAWILi9LKT3h25Vd8jr31gofIS53Z2f3FFvKsBL27f1PadZSBeISguPYk9Z42ohxO8mPiiDBNJARcHvDonh38c/e2XpOk5LBw/jD7opBMjKSU1LnbuOv9N6hq6Ttx77A4fwTfmDC1H0cWOmwh2Hi8ki+vet+v+HuKxvC50eMHdLMnISXvHznETzas7rU2HyDCNPntzAuZnJQy6DakUpTziUreFWUAPb3iS5RUrPY5Lj9tNrfPe7gfRqQEi5SSmrZWXijdx9ID+znR2tr52riEJG7OK2J+Znv/+1BL3KF906m/7Njic4ceHY1XFl5LSljEkJ+BF1Lym83reOXAfr/ih0dF8/zl1wR5VH2zpWB/XS2P7dnBR8cOn3WD6dR15mdmc9+IsWRERoXk76eiKP8xeHvBKcog09BynJKKNX7FllSsob6lipjwlCCPSgkWTdNIDAvn0yPH8ZlR42n0uGmzbSIdDiJMR8gv8BQSlh0s9T0OyfMl+/jCmAkYA7Yn6rmhaxp1Z/RO90VtALGBMDSdgpg4fjVzHjWtrbxVXsaJ1hZAIzUigoVZuUQ5HO27qA7xGzBFGQpU8q4oA6S6/gD41VQQQHKi/kC/Je8dbSk7CGmjMXh3lzyXOsoNYs7Y/CaUE3dLCD44csjvxHTZgRK+OGZicAcVBN3dMAV6E+UIILa7vusDpeP3MiEsjJvzi6DzRkvi6NhX4twMTVEUH6nkXVEGiGW39n1QLzwBxp9JSIGGhttqYlPpS5RVrqPV04jDCCM9fhTTChcTG5F2Vj94ZegRUnK4qcHv+Fp3G62WRUSAm1YFiy0lthC8U36AZQdLqWxpQkpICgvj8qwcrsnJx2WYPq9zsIRgeHSM3+MaHhXtd2wwOc7hTYSiKIFTn8iKMkAiw/zvZgIQ4YoP0kjaN2iyhIe3N/8/dhx6E8vuOuN6oGoda/Y+QUHabBZO+T7R4SldZuaVocfydQvfM+NlYPHB9Oz+PTy2Zwf1HneXf69oaWLHyRP8dedWbikYwRdGT/BpsbKp61yfU8ije3Z43b3ldNflFoZ8+ZSiKKFPvYMoygBJix9JVFiSX7FRYUlkxI8OyjiEsHFbLTz23qfYUvbKWYn7f0j2V6zikXfv5ETDAWzh2w6vyuChaRDnCvM73tA0Is3QmHV/cNtGHtq+6azE/XQttsXje3fy3+tW+VylnxQezpy0YT6PK97lCtlNuRRFGVzUu4iiDBQJk/Ku9yvU37ju6LrBsx9/lcq6Yq+Ob26r5d8ffRG31RS0MSihxaEbzB823O/FivPSM895a0FLCF4uK+bZ/Xu8jnn3yEH+sXtbZytRb6/zwNiJPt+sfHXclCG+nFdRlIGikndFGSC6bjCtYDEOM9ynOIcZzrSCxehBqFMVwqKkYg3l1Vt8imtsrWZ98RKEmn0fshLDwpmb7vuMMsAt+SOwAyy7CZShaTy+17uNsk63ZP9ePOLsXWR7Yuo6mZHRPDj7IqK8qPHXgK+Nm8wVWTnn/AZHUZShQb2TKMoACnfGcPPs/+f1AlBdN7lp1v8j3On/Irkzz7e+eIlfsZtKX2qvr1CGJFsI7i4a4/Ps+8i4BCYnp57TxNQSgjWVRznW7PvToSbLw+sHy3yq+Td1nTEJSTx28UIWZuXi7OFrn5qcykNzL2FxwUjVuUlRlKBRmzQpygAT0ubQ8c28tOZ7NLXV9HhchCueG2f9huHJk9C14HSHaHU38LulFyP9XFx4+7w/k5c6UyUiQ5SQkqVl+/nNlnVeNTVNj4jkXxddQazTec5nlX+0fhVvlx/wK3ZSUgp/m3eZz3G2lOhAo8fD64dKOdrUiCUFCa4wrsjKJTMqWi1QVRQl6FT7CEUZYLpmMDxpIl+7+i12lb/L+v1LOHxiG+094DUyE8cxtWAxo7Mu6zxeSolEdEniz+zN7o3G1hN+J+4A9c0VCGljaOqtYyjSNY3rcguIcjj53db1vW4qNDkphV/OuIBox7lP3IFTmw4NbGzHU4pop5Mb8wrR0NBo37jK0Nq/JypxVxQl2NQnsKKcAx1lM6OyLmVs9hUIKbDsNkzDha7pnYl5R415fUslG/YvoeLkXjx2Ky5HFPlps5iYey0Oo71LiKZ5kyQE9qBNBhivhD5N07hkWBYXD8vivcMHeamsmJL6Oty2TbTTydy0YdySP4L82DhsIQYkcfdms6VAdgYNxq6ip/dOV13UFUXpTyp5V5RzqGPmXNd0nKctZDV0E1tY1DdX8tamX7O/YjVnJt77j33M+9sfZnz2VVw+8RvomtnnotbIsETal9D5l4THhKege3WToAxmHQn5/MxsFgzP7fLa6Z1Z+jtxF1LSZlssO1jK6wdLOdbchJDy1GZLudyQW0icy4WUkrSISL+vkxYRFcRRK4qi9C9V864oIcgWFtX1pTz54edpcdf2eXx6/GjuvvgfmEZYn8n1vz96gNLKNT6PKTIska9d9ababVUZMP/as50n9u6ixe6+y5Gp6VyTk8+3JkxlT20N9334tl/X+eX0C7gwI1OVuCiKMiiodypFCUGtngb+veIBrxJ3gGMnd/Hcx9/oM3G3hcW0wsV+jWlS7nWgOlUPWZYQnbPqUko857j14683r+Pvu7b1mLhD+66uL5UV88/d2xiTkERRrO+7ECeFhXORStwVRRlE1LuVovQjW3g6F4hKKbzapVQIm7X7/k1T6wmfrlVWtY5DxzcjeulZbegmhelzSY8f5dO5I1xxTC+8LSi95pXQ0pG0r6o4wm+3rOMHaz/mZxs/4cXSfTR5PAgpEV4ucraEwG3buG3b777vlhA8X7KXl8u820QM4LG9O6lpbeWLYyag+3iD+dlR430doqIoyjmlymYUJcg6knWP3ca2A8s4enI3ltWC0xFFXuoMRg67BKDHRFgIiz8uu6LXNpI9GZ15GTfO/k2vxwhh0+pp4NH3PkVN46E+z+lyRHHnhX8lNW6Ez91tlNDVMcv+5L5dvFC6j6qW5rOOcRkGl2dm88DYSUQ7nN3OTne0S2yyPHxwpJyatlZMTWNYVDRz04ah4VttvJSS695aSkWLbz3bZ6Vm8IfZF7HsQAm/3rwO4cW6js+MGsenVfKuKMogo5J3RQkiW1h4rBbe3/4nth98A7d1dkIUFZbElPybmDvqftC0LqUuQtrsP/oxS1Z93a/r67rJt6/7qMvi197G+fqGn7P7yPtI2f1sfVbSRK6a+kPio7JU4j6ESCmxpeQ7n6xgVcWRPo9PDY/gLxdcSlpEZJcE3paCA/X1PFm8i/cPH6LtjKc+Ca4wrs7J547CUUSajj5LUywhWFt5jG+s+dCvr+v3sy5kdtow1ldV8LddW9l1svunV7nRsdw3ciyXZ+X4dR1FUZRzSSXvynlFCLt9Zlxr75/eW3vF9t7q0uvuKrawaG6r4YkPPkNNY3mfx+ekTOe2Cx7C0M3Ocdi2h02lL/HW5t5nz3vz5StfIy4yo8/jhLDRdYOm1ho27H+O0spPaPM04jDDSY8fxbTCW0mOyfOrn7wS+nzd1CgjIoon5y8kyuEE2mfu3zt8kJ9uXNNnfXxqeAR/mjufYZFRfSbwP16/irf83GxpbHwij1x8RWcbyX21NSw9UEJFcxMSSAwL48rheUxMSlGbJymKMmipT2RlyOsoY7FsN8XHVtDYWoOuGcRHZZKXOgMhRWdy2pGo1jVXUHFyL5bdSpgzhuHJkzB0Jxo991MXwsNTH33Bq8Qd4EDVOl5c810Wz32w63gD7qXuXXxH2U5kWAIXjP4MF479/H/OcFqNs0rchxYhJaX1tT7vRnq0uZEl+/dyz4gxAKyvquDHG1ZjezH/U9nSzBdXvsvjlywk3hXWa1/1k209bwzVl5pTsR1JeUFsPN+cMBX91PWklJ3lQipxVxRlsFKfysqQJoRNXfMx1ux98lQZS9c62piINKbk3cTMEXdi6A72H1vFhv1LKK1cy+lJsMsRxbjsK5lReBtxUZlnzcbbwmJjyYtU15f5NL59Rz+ivHorwxLGouvtTwKiw1P8/no1TSfcGedz3Jn1995t+KQMVktK9voV98qBYu4dORaAX21e61Xi3qG6tYWHd2zmx1Nm9Xqcqfvf0ejMWP2MmwRN04KyIZOiKMq5pD6hlQFjCw+2sDq7rggvOq8EQkibkorV/P3tW9hY8vxZiTtAfXMlsZFpSCTPr/4Wz636OqWVn3Dm7HWbp5EN+5fw17duYtuB1ziz2szQTTaWvODXONcXP9uZPOu6QWH6XMKcMX6dqzB9Li6H/5vVKEOfJQTv+FmWUtXSQoPbzaqKI1R2s8C1L+8dPkiT5enxdSElGQFsmDQsMtrvWEVRlMFCJe9Kv5JSIoSNLSx2lb/Lu1v/yOsbf8H72x6itHJd+8K5fkjibWFRXr2V51Z/E4/d2uNxUwtuYUzWAl5c/V32Hvmgz/MKabFs/U/YeehtxKlFnlJKyqu3eNW5pTu7j7xHm+c/Nxa6ZjAh52q/zjWt4NZ+vylSBrc6dxutds/tRHuTHRVDrMvFC6X7/Ip3C8HSsv1YvdTI35BX6Ne5Aa7Nye/13IqiKEOBKptR+o0QFpbwsHLXP9lS9grNbbVdXv9k31PERQ5jasHNzCy6EwleLw7ti6GbLFv3kz4SWY0Zhbez9cCrFB9b4dP5l234Hwoz5uJyRCGkzYkG/xJ3aP8+NbYe75wx1zSdmUV3srXsVVo9DV6fZ1jCOPLSZvo9DuX84Eupy5lSwiMA2F9X6/c5iutreyxd0TWNvJg4xiUksb2m2qfzJoeFMy8jS5XFKIoy5KmZd6VfCGHR6mnk0fc+xeo9j52VuHeobTrCu1v/yLMffw0p7bPKUfy9dknFGk42He71uLzUmSREZ7Fh//M+X8OyW9lU+vKppwYSIQOb7T796YOmaUSGJbJ47h9x9NHysUNidA63zXuo1w2aFAUg2uH0e5/cMLO9vKvNz5n7jlitlwTbEoIvjZ2E6eON/BfHTgTVPE1RlPOASt6VoDiz/EVIwb9XPEBVnXe7JO4/9jGvfPLfvX6oe0vXTTbsX9LncZPyruPwie1U1O7x6zqbSl7A0E10zSDSleDXOTpEuLpu627oJpmJ47j3kkfJSBjTY5ymGYzOvIz7L30CpxmpdkBV+hTpcDA9Jc2v2I6kPcrh8Pv60Q5HZ8eX7pi6zrjEJH46bbbXCfwDYyayMCvXp82gFEVRBitVNqMERAgLXTdpaDnO5tKXONl0lEsnfJU9h9+n4uRun8616/ByZp64k/T40QEnoRUn++6mER+VyaHjm/2+Rk1jOa3uBsKc0eSnzSbMEe1TmUuHzMTxRIUlnvXvum6SFJPH/Zc+SUXtXtYXP0tF7T48VgsuRxT5abOYWnALUWGJnT3bFaUvlhDcnD+CtVUVPsfmRMUipGR2WgYvlnp3Y36mOWnDEFKe1QnmdIamc/GwLFLDL+Xvu7ax4XhFtw1QR8UncN+IsVyQnhmUG39FUZTBQCXvit+EsDleX8YH2x9mf8UqpBQUpl9AVFii351X1hU/w3UzfhHw2HpbpNrBaUbgsVoCvk4Y0ei6yficq1hX/IzP55hacEuPGyF1/FtqbCFXTf1hlxaOHTdOcHarR0XpianrzEkbRm50LGUNdV7HOXX91GJSyeL8EX4l7ynh4VyQntlr4t7B0HRGxSfy8AXzOdLUyNID+6lsbkJISUJYOFcOz6UoLgFLCJW4K4pyXlHJu+IXIWwOVK3nuVXf6JIojxm+gMMntntdLnOm3YffY9GUHwTc7tDliKLF3Xti4vY043REBHQdp9ker6Exo+gONpe+7NWNQ4e4yGGMzrq8z42Quuu7rqvNkxQ/CSl5aO4l3P/hW1S19H0Da2gaP5k6h+FR0eiaTnZ0LNOS01h/3LfZ+5vyRvQ56366jo2UMiIi+eyo8Z3/bZ+WsHe32VJHWY5HCE60tuAWNjEOFwlhYWpnVUVRBj31Dqb4TAiL4/WlZyXuAJGuBGoC6LxiCw/1zZUBjU9K4VXXleqGMrKTp/h9nZTYwtM6xGjEhKdw0+z/h6F7Vw8c4Yrnjgv/4vf1FcVfpq6T4ArjsYsXMimp903BUsMjeHD2RVw8LKuzptwSgp9Nn0N6hPc32RekD+OuotF+Jc6apnWJM3S91xuAAw11/HbLOha89gLXv72UxctfY+EbL3LP+2/y5qEy3LatWkoqijJoqeRd8Zmum3yw/c/dzjAbugNb9LwJizcCjQeYXnhbn8dsKn2J1LgiMhMn+HWNKfk3d1mkq+smeWkzuePCvxIXmdFrbEbCGD596VPERqT3OeuuKP3B1HVinS7+Nu8ynp5/JdflFJAaHkGkaZLgCmNmajq/m3Uhr1xxHVOS07oky6auE+1w8shFCxgRF9/LVdotGp7Lr2fM688vp31PCSn59eZ13Pbu67xctp8Wu2sXqD21Nfx80ydc//ZSyurrVAKvKMqgpLIGxSdSShpajrO/4uNuX2/1NBDhigvoGuGu2IDiNU0nOSaP7OSpHDy+ocfjDh3fxPG6EqYVLubwia0+XcPliGJC7lVnJd66ZpCZOJ4vLVpGScUaNuxfwtGTuzoXmeamTGd60W2kx4/qsc5dUQZKx2x2bkws3500/azZbEsIdE3rdpa7I/l/4pJFrK08xvOle1l17Cji1NLSSNPkiuG5LM4fQXZ0LFLKfq1N1zSNn6xfxVte7B5b3drCZ1e8wz8vXEBOdIwqo1EUZVBRmYPiEyFtNpe+hJTdz1iVV29l3pjP4nJE0eZp9Pn8aXEjiI3wr43d6YSwuGHWr/i/5XfQ0FLV43Gr9zzOtTP+h5Jjq9h28HWvzq1pBjfM/DWG1n15TEdCnps6nYL02V1ek1IiTyU3KnFXQkVPJSh9JbUdr09NTmVGajpttkWD24Opa0Q5nF3O25+JuyUEbxwq9Spx79BsWXx7zUe8uOCafhuXoihKf1DTDYpPDN3kZNORHl/fUrYUXdMZn32lX+efcqrzSqB03STcGct98x8nOSavx+O2HXyNdcXPcvW0nzAx97o+z2saYdwy5/fkpc3ss8NLd8m5pmlB20VWUUJFRy28yzBJCg8nzhWGeaou3dvFqYEwdZ3nSvb5HHe0uZHVFUdU+YyiKIOKyiIUn4lekusWdy27ypczrfBWTMPl03mjw5MZn31l0GakDd0kKiyRzy5Ywi1z/kBuynQ4bW9JQ3cyPvtKMhPHo+sGV0/7EffNf4Jxwxdh6M4zxpbChWM+z1evep2CtDkqAVfOGVsKPMJWiy5PsaVkZ001xXUn/Yp/rmSfKptRFGVQ0WQw9qNXzhtC2Lyz9fesL362x2OSonO579LHKa1cy0trvo+Qfc+kuxxRfOrif5EYk9Mv5SQd9eWt7gaa3bUYmklEWDym7kIi0LX2WXQhbDRNp81qoqbhEB67lXBnDEkxuSClas+onDMd9efrqo5xsKEejxDEuVzMS89q37UUiXEe3lS6bZsn9u3kn7u3+xVvaBqrr789yKNSFEXpPyp5V3wihM3Rk7t49L17ej0uL3Umi+c+yIGqDSxb/1MaW6t7PDYpOpeb5/yO+KgsVQeuKGewhMAtbJbs38srZfupaGnq8rpD17lk2HDuLBxFQWz8gJSphBJLCB7esZln9u/x+xwfXbOYMFO99yiKMjio5F3xyz/eXkxlHxsxZSVN5JY5v8fliGLP4Q/YWPJ8Z+cVpxlJbup0phfcSk7qNNV5RVG6YQlBTVsrD6x8l0ONDb0ea2ga3580gyuz886rBN4jbB7ZvYNH9+7wK14DVl9/+3n1PVMUZXBTybviM1tY7Dn8Hi998v0+j3WY4YwbvoipBTeTGld01utC2H0u/FSU85GUkkaPh3s/eIvypt4T9w4a8Ivpc7ko4z8bKg11lhCsqjjCdz5Z4Vd8YWw8T81fFORRKYqi9J/z491dCSpDNxkzfAFzRt7b57Eeq4VNpS/yxsZfYtmes1pMqsRdUbongP/bvc3rxB1AAr/eso7zaRmrqevMTc8kJTzcr/gb8wrVwl9FUQYVlbwrfrtk/Je5fOK3cDmiejxG0wzGZV/JXRf9A13T0c7DBXWK4g9bCF4/VOZzXL3bzTvlB86vhFRKrs0p8Dks0nSwaHie6jajKMqgospmlIAIKRDCw7YDr7O57BVqmw5jC4tIVwKjsy5jWsFiosKTkFKoxF1RvOQRgjcPlfKLTWv9ih8bn8gjF18R5FGFtjbb4rMfLWdPbY1Xx2vAz6bN4ZLM4edllx5FUQYvlbwrQdHdglNVzz74nP5zPP1/q5/lwLKE4H+3b+K5kr1+xbt0gxXX3Yo49fZ+5mJMS4ghN9tsC0GT5eErq95n98neE3hD0/jepOlcnZ3frzu/Koqi9AfV3kMJiu46xahkb/AQUmDbbrYeWMamkheoqi9FShuHEUZhxjymF95KVtJE1RVogOiaRpvt307DkaaDX8yYC8DhxgaWlOxlxbFy6txuXLpBbkwsN+QWcmlmNhoMmYWthq4TaTr4x7zLefVACS+W7aO0vq7LMS7D4LLMbG4rGEleTJxK3BVFGZTUzLuiKGwpW8rbm3+H22rq8Zi0uJEsnvsgkWGJKoHvZ5YQ/HXnFp4q3u1TXLTDyV8vuJSk8HB+vH41a6uO9XhsnNPFN8ZP4fKsnCGXxHY8WdhRU01pfS1uWxDjdDInbRgRponk7KcRiqIog4VK3hXlPCalYO2+p1m+9Q9eHR8VlsR9lz5BVFiSSuD7kS0EW08c5wsr3/U6RgMenjufvJg4PvvRO153qfnWhKncmFc0JJNZKSW2lEgkOtqQecqgKMr5Tb2TKUo/kNZ/7ok77o+llEgROvfKQliUV29h+dYHvY5pbK3mmRVfRtdUSVR/MnSdycmp5ETHeB0zKzWDqSlpfH/tCp/aS/5h60a2njg+JLvTaJqGqes4dEMl7oqiDBnq3UxRgkgKifRIrI/baPlxLU33V9N8zwmaPn+Ctr81Isra65ilfe6TeF03WbP3Cdq7g3vveH0JpZVrEMK/mmzFO5YQ3JI/wuvjb8wrYuuJ42w5cdyn6wgkT+7bOeQWsCqKogxV6t1aUYJESom1uo3mL9fg/tepRN1z6sVmib2mjdaf1tHyk1pkvTznCXxDy3GKj33sV+z64iXoqmymX5m6zo15RVyemdPnsekRkcxOy+DF0n1+XWtNxTGqWppQVZSKoiihTyXvihIEUkisd1tx/6MRmntPgESpRetPapF14pwl8LbtYffh987a8dZb+ytWYQtP3wcqAZFS8tNps7k+t7DX4yYkJqNrGh8cOeTXdQSS5eUHsfz8fVAURVEGjkreFSVA0paIEgv3Uz13ajkr5qSg9Xf1aMa5WSQokbS46/o+sKd4KWjzeP/1Kv7RNA39VE/y5y+/mpvziog0HV2OmZqcyp1Fo2mxLNwB1K2fdLfRvuy1/3iE6Ow9bwsxJOvsFUVR+pt67q0oAdIMDc8bLb6WjiMP21jb3RijHeckiTd0R98H9WO84pvMyGi+MWEqXxs/hTp3G5YQRDmcRDoc7YlwgCUvpqbj8y+xFzqS9Tbb5vVDpRTXnqTNtolyOJiTNoyZqenYUqqae0VRFC+p5F1RAiRqBfZmt1+x1vJWzHHOII+ob7qmkxSd43d8pCsBpxkevAEpfepo5ahrGolhXb/3hq5jAIlhYZxobfXr/JlRUQR75t06tevp33Zu5a3yMpqtroucny/dR0ZEFDfnF3FbwUjVf11RFMULaqpDUQIgLYm9vg38fPpvb3V3aSs5UHTdpCjjQiLDEv2Kn5h3nVrcGGJsIbgmu8Cv2EjTwWWZOTiCOPttCUF1awv3vP8mL5UVn5W4dzja3Mj/bt/Ed9euaG+nqn6vFEVReqWSd0UJhATZGECyIYGWc5esTMq9zucYTdOZVrAYXVe93kOJoevcnF+E4cfM9aLhuUFN3KG9TOaBle9yrNm7tREfHT3MLzatHXK7vSqKogSbSt4VJVCB5rDnKAfWdYNZI+4hPirLp7hZI+4mOjy5n0alBCIxLJzbCkb6FJPgCuNTI8cGdRyWECwp2cvhpkaf4l4/VEpZfV1nnbyiKIpyNpW8K0ogdNDTA8i+ozUIP3czjQ4zjLsu/BvxkZleHT8570bmj/9KP49KCcSXxk7iquw8r45NcIXxp7mXEOd0BbXWXNc0Xikr9it2ScneoI1DURRlKFLJu3LOSSmRVvumRdKS56QG3F+aoWFMdkKMf4mPY16Y3/XywWDoJlHhydx/2ZPMKLqDMEd0t8elxY3kmun/w5VTfzDAI1T88cMps/jOxGlkRXX/83TqOguzcnns4ivIiY4NaqcXW0rWVh6jsqXZr/i3y8uwVQtJRVGUHmlSrQ5SzhFpSzRDQ9TYWKvbkHUCNA09Scec62qfkZag6aFdAyttieelZjzLWnwL1CD8wXi0eP2c1/lKKZEIhLDZeegtqupLsOw2whzRjBh2ERkJY7CFhaF2VR00LCEwdZ0Nxyv48Eg59R43Tt0gNzqGa3IKiHI4EOBXjXxvPLbNS2XF/GHbRr/P8drC60kOjwjiqBRFUYYO9UmsnBNSSESZhefVZuytnrPaS7uXNGFOd+G4NgKS9XO2mVFfhLA5enIn6QtHYX3Shjzu/YyhuSAcPSE0Fn1qmoaGgW4YjM1e2P5v6EgEmtY+K6sS98GlYzZ9YmIKkxJTMHQdISW2lJ2LU/vrty/QnvOBxiuKogxlqmxGGXBSSKyP22j9eR32lrMTdwA8YK1qo+XHtYhiD9IO3Q/zpet/zEnrCM7vRKIlefcnZc5z4bwtAiHsfh6d7wzdgaE70HWj/f9roXGDofjH1HWMU8m6rmlB7ypzJk3TiHe6/I8HYhwDv/eBoijKYKGSd2VASVtib3HjfqTRu1rvFknrHxoQx+yQS+CFsNh79ENqGg7y75VfoM5VifMnUZiXhfW4CFXLNHDeH4nr09FsKn1JtVtUhhxT17kwIwuXn7/bU5PTiHSo3XsVRVF6opJ3ZWDp4H6yybdd2FslnmeaQq50RkhBdX0ZAPUtlfzrg7vYdeIdzNtdhD0Ui/PeSMzLwzAvcuFYFI7zv6OI+GU8LZPrWbb+f3hj4y9wWz7WySvKIBBhmlyame1X7M35RVhqwaqiKEqPVBGrMmCkLbG3u5EnfP9gtnd4ENU2WuK5X9zZQdM0bOHu/O9WTwNL1/+I5dv+wMTca5kw8VqiZydjGi7a3E0cq93K+pVL2F+xCinbvwe28ADhPVxBOd95hMDQNHRNw5YCKQlqZ5j+IoE7i0bxzuEDeHxIxLOjYrggPTOobSsVRVGGGpW8KwNGMzSs91v9C5ZgvdeK4+aI9qLYECClJMwZe9a/N7fVsnrP46ze83iv8Zqm4zRVRw2lq44Nitpsm9cPlrKvroZW2ybK4WB2agaz04YhpAzpJF7XNLKjY/jhlFn8ZP1qhBeP2hLDwnho7iUIKVXyriiK0guVvCsDShzzf4GmOGaHVNtIQ3cwatglLN/yB3yrA2pXkDZHdXBRurCEoMny8NedW3jr0AFabKvL6y+WFpMWHslN+UXcUTgKIGQTXUPTuSwzm0jT5Jeb13Kitecb97Hxifxq5jwSXGEhfVOiKIoSClTmoAwsq+9DeuQJrQWrmqYRG5lOftosSipW+xw/rXAxQljoKoFXaE/cq1tb+PyK5RxrburxuIqWJh7esZkt1VX8ZuY8NAiZUrIz6ZrGrNQMll1xPR8eLefF0mL21Z2k1baIdjiZmZrO4vyRjIxP6OxLryiKovROZQ3KgNKiNOQJP4OjQu+D3RYWs0bc5XPynhSTS17qrJBNupSB12bbfGnle70m7qf7uOII/7NxDf8zbU4/jywwHW0qL8zIYv4Zi1jbNwdr19+Je8fNQbPlwW3bRJgOnIahbhoURRl0VPKuDBhpS4zJLsRB/7ZNN6c5O3dlDRWGbpKTMp2Lxz7ABzv+7FVMpCuB2y74E0LaGJr6E1TaE8vnSvZS3tTgU9zb5Qe4u2g0eTFxIVs+06G7BLl9c7D+JaSk3u3mpbJ9vFy2n6qW/7z/TE1O5eb8EVyQnokM8XUEiqIoHTQp1VZ2ysCR9YLmr9aAj6XvWrxO+IPxIVXzfqZ1xc/y3raHsOyea3tTYgu4de7/EhWerOrdlU5CSq5/aykVLd7Nup/umpx8vj9pRsgn7+fKk/t28bedW7Fkz11vMiKieHD2RWRGRasEXlGUkKeSd2XAtf29AWtVm08xjlsicCwMD6lZ9zNJKXBbLWwufZlNpS9youEgALpuUpQ+j2mFt5KTMhVbWCpxVzoJKVlXdYyvrvrAr/gww2D5VTfjNNSGX2f6x65tPLJnu1fHRjkcPHLhApXAK4oS8lQGoQwoKSTOe6MQx2xEqXerV43pTpxXhX5LRU3TcTkimVZ4KzNH3IkQNrb04DDCABCi/XGDStyV09lSUN7oW7nM6Vptm5NtraRGRAZxVIObJQQbj1d6nbgDNHo8fG31B7y44Np+HJmiKErg1PSCMqA0XQMDwr4fizHZ2fvBOpiXheH6YjSD6QFRR3Ku60Zn4t7x34rSHTvA329rEP19DART13l6/26f4441N7HiaLna4VVRlJCmpgCVAacZGlKThH0tBnHMwvNOK9aaNmhuT0C0eB3zAhfmpeHocer+UhnadDTinK6AzhHj7ONG+DwipaSipYm1lcf8in++dB8XDxse5FEpiqIEj0relX4nhIWmGbR5GrGEG5cZicMMwxYWeqqB865IXPdEIS0J2qnkPsS6yihKfzF0nXkZmYQZBq2275uYTUlOJdqhkvcOlhSsOHrYj23T2m08XkmbbeNSawgURQlRKnlX+o2UghZ3PRtLnmdTyUvUt1R2vpaZOIFpBYsZlXUpAAYmmvmfZF0l7v1PCBuJRNN0dE094TiXwgyTy7NyePVAic+xN+cVqV7lp5ES6j3ugM7RbHlU8q4oSshSybvSb9YWP8N72/4XIc5emHr4xFYOn9jKu9se5Na5/0tKbIHaaXQAdHS6Odl4mMMntuG2WnA5IslNmU5kWAJC2Ko2/xy5o3AUbx0qw+1DvXV2VAzzMrIwVJvILlwB/g471I2QoighTGVLStBJKVm1+19ebVrU0HKcxz64n3su/j9SYgtVJ5Z+IqVE0zT2H1vJuuIlHKha1+V1XTMpypjHjKI7GJ486RyN8vylaxpZkdH8ZOpsfrh+lVcLWBPDwnho7iXti7lV8t7J0DQKYuP8jk8OCyfSdARlLB1PRI42NVLe2IAlBUlh4RTFxmOrTaEURfGT6vOuBJUQFodPbOfxD+73KS4qLImvXPU6hh6cD03lP6SUSGnzytofsrP87T6PnzPyXi4Z/+UBGJlyJiElayuP8cvNn1DV0tLjcWMTkvjVjAtIcIWpBLAbgWx69elR47h3xNiAvq/2qacn7x85xAul+9hy4niX14dFRnF9biHX5xYQZpjqZ6goik9U8q4E3XMff4O9Rz/0Oe6a6T9l7PCFava9H7y05vteJe4d5o3+LPPGfA5NzegOOEsINE1j5bHDvFi6j321J2mxLaIdTmamprM4fwRFcQmqzr0XlhA8VbyLv+7c6lOcoWm8vugG4l1hfR/cA1sImi2Lr63+gB011b0eG+9y8eDsiymMjVc/S0VRvKaSdyWoGluq+eNrC5HS964ZGQljuP/SJ/thVOcvW1iUVKxmycdf8zn2gYWvEB+ViaYWs54T3SXnUkok7WU2/XEdISW6piFOfSwE8zoDrdWy+PRHb1NcV+t1zOdHT+BTI8b4fdMqpcQtbD794TvsqzvpVUykafJ/Fy1geFSMSuAVRfGKeqdQgsYWFsXHVvqVuAMcrdlJq9v/nSaVsxm6yfriZ/2KXVf8jN/t9pTAdZfIaZoWtIRaSEmL5eHF0n3c9u5rzH75aWa9/DSXLnueX2z6hP2nkk97kG5Y5NB1/jR3PoVe1r/fM2IM944cG9DTJgH8bedWrxN3gCbL4gfrPlaJu6IoXlPvFkrQtLeGDCz5bvM0Bmk0CkBt0zFKK9f6Fbvt4OsI4QnyiJRQ8VzJXq54/UX+sG0jpfV1nYtkGzxuXjtYyl3vv8nnVyyn0fIMyh1HDV0nxuHk/y5cwOdHTyA1POKsYzT4/+3deXRU150n8O+9773atCAJEPsuIRZjYwgQbAMGGzs4Trx0nN1xMknOxOnjOH08nUkvmSTdk05OJ+NJp93p0xmn43an3d7SncXu2LGxHQI4MY4NhgSwxW6BjEEgJNX63v3NH4WIEEiqelWS6lV9P+f4+IDer/Sko0LfuvW7v4vljRPx7SuuxmcXLi74c3rG4GeH9uddt/9MJ3acPB7YF0pENLLYXExFo6DgWIWdFGkXWF+KjPFgzr4boZU1YqMYRQzaTu4EfK6fpzLdONXdhvFjZhf3xmjUfe/3r+H7e3YOed2rJ47jE88/hR+sfReqbQdWwFaHLa1haY3b5y7AHS0L8eu3juKNztNIeR5qHAerJ0/DlKrqorw4yRiDXxw5iC6fM+YfbX0dl61oLPg+iKj8MbxT0WhtYUJdi+/6WLge0dCYIt7R6Mn2JhsY4+H1o7/EmcRxKCjUxiZg7uTVUEpDQQ/rhlAjBhkvWdBjpN14ke6GSoFrDLa2t+UU3Hu19XTjCy9uwj+tWT/o4/Zv+yilDbW997GicRKWjZ+YXXKXP/x9Me7TVgrbTx73Xf9ah/9aIqosDO9UNEppTB+/GONqZuFE14G86xfPumkY7mrkecZFPNWBF/f+K3Yc/BmS6TPnfTwaGoPLZr0XK+fejmi4btim62hlIexUFfQYkVBNke6GSoGtNX74xu6867afPI69pzvQPKb+vJ571xhYSuE3x4/hJwdbcbSnB0YEDeEI1k+dgQ3TZ8HWumQ2vlpaY7je91JKoce98EC6XPVk/NcSUWVheKei8oyLpU3vw9OvfjOvOqU0ljV9sKiTTcQVKFtBEgaSFKiQAmIKMICyhidMGOPiaMfv8PDmuy8I7b0S6U78eu+/YseBn+FDq76DifXzhiXAK6Uws/EdsLQDz0fvek20EfXVU4t+XzQ6jAgOdZ3Bjn4zx3P1cOse/K+lK8/9WUTwXNthfPd323Esfv489X0Atr3dju/segUfmNOCT8+/tOhTckqNiKDK9v88rnL465iIclMa72lS2bC0jaWz34cpDYvyqls1/1OoiY4vShuJeALJCNwXU0h8+TTi/70DibtPIX5nBxJ/cgqZJxKQLoNiT0n1jIsTXQfx0KY/HjC495VIn8a/bfosTnUfgWeGZ9Ut4tRi/tRrfNUumX1r0b9HNHo8MdjYdsh3/QtHj5x7fooIHmrdgy9t23JBcO+rO5PB9/fswl+8tBnlG9uzXBEsHuu/Z/3SBva7E1FuGN6p6JTS+PDq+zC54ZKcrn9ny+1Yc8lnihbczQEX8c93IP3/umEOnB+KpcMg86M44p/rgPtMYf3g/VnaxpMvfy2vPvFUphv/9duvD1vrjIjBirkfybvO0iG8o+k2HphVRhQUTqdSvuvjrgvXGLjGYHN7G/5+5ys5125sO4x/+N32c/Pjy5GjNa6bNhM1TshX/fub5sIz2UUFIwLPGJxOJXEymUDKy254D+LUHyIqPv5mpqLT2kLIjuGOtd/HttaH8cq+x9HRfaTfVQqzGpdhxdyPonnyVUX5vOIJzH4XyW90AkN1iXhA+oc9kJTAuTFa8AsHEYMTZw7gzZP5negIAIfefhkdXYeH5UAkrS1Mql+Aqy/5LF7Y9d0cqxRuXvG/EXFqi3ovNPqcAjZmKmTbXrRSuH/3zrxnGD26by/+27xLELMd3/dQ6iyt8Z4Zs/FQ65686ppq63DZ2VX7tp4uPNK6F08e3o/uTPYfMg2FlRMn4bbZLXjnhEnwREpmMzARjTyGdxoWWlvQsLC8+UNY2XI7Drz1Eo6d2gPXSyLsVGPu5NWor55a3HYRD0j+3zNDB/c+Mo/HYc1zoGfbBffBb2t9pKDa9YvvGZbWAqUUVi34FCxtY+Nrf48Jdc2oi02GY0eRyvTgxJn9ONXzJoDsqM6blv815k9dx5NVy4xAML3G/wuyqdU1EAB7TnVgz+mOvOtTnoefHGjFbXNayjZ4agCfWXgZfnviLew9nfsJq99ddS2MCL65fRv+48AbF1xjINjSfhRb2o+iqbYO375yLerDkbL9PhLR4JSwqZVGQN9Z59lTIu2ijkkUV+BuSiL9wMD9twOxloUQuavwVeYfbPw43jz5mq/a6eOX4I619xd8D4MRERjXheVcuPJ55Ph2dKdOYEbjMkSc6hGbRU8jK+G6uOG/foS4j6kod11yOT7cPB/37ngZj+1/3dfnn1fXgH9Zt8FXbVB4xiDuuvj81uexq+PEoNfWh8N4YO0GTIxV4S9f2oxn3sxtT0JjNIofrN2AulCYAZ6oAnHlnUZE70r8cFG2grvRXw+790oa0mWgagr7JVjITPWMW9z++/7Ey75Gl1cNEhs7s3sBUgJEFayFDiavXwhrngPxBEqX+9bCyhW2LFw/bRb+8yKru4PWaQs3z2qCVgonkgnfn//tZPmfG2BpjZht43ur1+O5tsN4fP/r2N5vws+UqmrcOqsZN89qQtiy8ei+vTkHdwA4nkjgi7/ehPuvvr7Yt09EAcDwTmVBOg3MEc9fsQe4O9KwV4YLap2JOP5nog/nPHUxAm93BunvdUNO99vwFhd429LwtqWhpliI3F0LjNfDNkqTRt+n5i/CpmNHcDKZ+wvGT85fhKqzveqF/GSosp85k9V7Eu3aKdOxftpMHO3pxps9XcgYg3GRKFrqGs47xOrR1r15f46dHSfw+ukONPWbvU9E5Y/vt1FZkESBUxh6BHnvwOvDMy7mTFw59IUDmDPximEZFymewNueRur/nLkwuPe/ts1D4iunIe3euZV6Ki9aKdSFwrjvqmswNhLJqeajzQtwR8tCKKVgRNAY83/wV2M05rs2iHrD+eSqaixvnIQrJ05BS13DuY+5xuClt47hSE+Xr8d/ZN/eCnk5RER9MbxTeXAK/BUWKqze0jaWzHkfLJ3/JA3bimDJ7FuKPpZRRCAdBqnvdgG5vikRFyT/9gzAiXRly9Ya06tr8eDaDbhlVhOi1sV/7hbWj8XfLL8Kdy26/Ly/f++MOb4/97unz+a4w35+1d7mu3Zr+9Gi7h0iomBg2wyVBVWngWoFdPtbMdYzLBTakh9xarBw+rvw2sGf5VW3aMYGhGz/q5kDEiDzVAJI51l2ysDdmoJ9ZRjKZjAoR7bWaIhE8YXFy3H3oiV48vABHO46g7QxGBMK4dqpM9A8pv6CoK2VwpwxdbikYdyQmzH7i9k2bpwxmxss+7C1RlcmzydoH4XUElFwMbxTeVCAfVUE7lP5b6bT0y1YswufPS0i2LDki3jr1B681ZnbhsCJdfNw/eV/CoEUvx/YBdwt/g7lcTcm4KzJra2Cgqm3TzpqO7hpZhMUAKUAI4B19mMXC9quMfjMgsvwuc3PweTRa/bR5gUIWZxi1JcRQaSA70mY30+iisQlECoPCnCui/jaTWdfE4G4+a3YiyvZ/8zZ/3sCrTUs7eCOdd/H9HFLhnyMmY3L8LG198NSNnSRZ6qLCLydaSDu750Ic9CD6fC5AZgCx9EattawlIaj9aAbIG2tsXT8BHzx8uXQOT7h3jtzDj45fxE3VvbjiaCptt53fVNtXfFuhogCgyvvVBaUUlDjLDjviSLz09xX33WLDXt1JKfpKiJnN7V6gPvrFMwRF0gDKqZgXR6C1exAGwuOFcMd6+7HoeO/xbbWh7G37Zcwkt2MqrWNeVPWYVnTBzB9/OUw4kGrYVg9M4B0FbbpVLoFaCjS/VBZ0UrhPTPnYHw0ivt2bce+M6cvet24SBQfaZ6PDzfPh4iwP7sfR2u8e8Ys3LfrVSS8/Des3zp77nlTa4ioMjC8U1kJva8Kkha4Tw09Bk+32IjcMyan1XrxBEgJ0j9OwN2cvKC3PvNEAmqaBee6KJw1EYgIpo1bjBmNS5F2E0ikOwEAsVAdHDsCY7Kr2sMS3HsV+OxW/NeBBqGVwvLGSXjo2inYefJt/PhgK472dMOIoD4cwXXTZmLN5GnoPQeQwf3iwpaNd02fif880JpXXX04jGumTGdwJ6pAPGGVypK7PY3M0wmY32cuGAGpJltwronAXpdtsxnqUCLxBNJpkPxGJ6R96EkZ9lVhhD5dPephxdufQfIrnf6KbSD2Dw1QUQYDGppnDLRS5/3Mc0U4N0YEnekUPv7cU2hP5HZCtALw9RWrsHrS1HMz5YmocjC8U1kST6AsBXPcg7cjDUkIVFhBz7RhtTgQV3KepCJJQeLLpyHHcu8Bt9dHEL692u/tF038z05B2vLvXbfeGUL4zppRfwFCVAlcY/BWIo4//tWzOBYfPMBbSuHPl6zADdNncw8BUYVieKeyJiLZGeeC7HKVHnql/bx6T5B5IoHMj/I81l0B0W/VQ43ToxaAxRW4LySRfjC31by+Il8aAz3Hzut7RUT+ucYg4bp4qHU3fnKw9YITcB2tsXbyNHykeQHm1vFUVaJKxvBONAgxgsQ9pyAn8z9Yxr4+gtCHqkY1AEtGkPyr0zCHcl99t5aFELmrdhjvimhoIgIDgdVnEpOIwIiUdauId3a2/ub2Nhzp7oIrBnWhCNZOmYZaJ1T2Xz8RDY3hnWgAYgTergxS3zrj7wFiKts3nsMkm+EingA9gsTXO3Nqn7EudRD+fC1gcYMhjQ7PGFha41i8G4+27sVvjrejO5NGxLLRUteA2+bMxaVjx5d9T71nDLw+m32dMv5aiSg/DO9EA5CMwN2YRPqh/NtOesXua4CqHd1fuuIJkAEyP44js+nCSTkAoMZp2NdE4GyIZv/MdhkaBa4x6HEz+OrLW7G1/eiAR0DNqa3Dl5a+E3PH1HMVmogqDsM70QAkI8g8nUDm0Tz73fuIfrseumH0T0E8N6PenJ1Rv98FUgLEFKyFIViXOYDBqL5LQJXNNQad6RQ++cLTQ27aBLKni9678mosHtdY1ivwRET9MbwTDUBcgbs5hfQ/d/t+jNj3xkJFSisQiyvnNu/ibCs/QzuNNiOCjz33c7zReSrnmirbxkPX3ogJ0RjbvIioYnC5gmgAylawl4YAnwvneoFTcsEdyH5dysrO5FaWYnCnUecagy3tbXkFdwDocV38e+se5L+dnIgouBjeiQahajSspSFftc61kWy/eRH0fxwxAr5pRuXC1hqP7XvdV+2Th/afm9BCRFQJeAA60SDECJwbovBeTiOf5T01QcNaEipo46eYs+0tcUHm+SS8XRkgIUAI0DNtONdGoSZY5w6kIgqqrkwaLx0/5rt2S3sb1kyextnnRFQRGN6JBqF09lTW0MeqkH4gx6kzNQqRPx2DAUdl5KB3Qkz6wW64v0kBmfM/bva6cJ9OQs93EP5ENTBeM8BTYJ1OJQt5uuBkMglPDLQa/c3hRETDjW0zRENQWsFeG0H4zmogNnhA1tMsRL9aBzXWf5gWI0BSkPir03A3Xxjc+zK7M0h85TTMEa9oLTpEI02rwn4VZVfc+eKViCoDV96JcqCUgrUijNg7wnC3pOA+l4A54gEegDBgXRqCc10UVotTcBuL0gqJezshb+Z4KmpckPxmJ6Jfr4eqZYCh4GkIR2ArDVf89a5PilXBYssMEVUIhneiHCmtAA3YV4XhXB0BgPOCeu/KdyHBXTyB9/sMzBtufoVdAvfpBJxbY2yfocCJ2jaunjwNz7Ydyru2MRrF8gmT2O9ORBWDbTNEeVL2H0JC36BcjNCsLAX3maSvWneTvzqi0eYZg/c3zfVVe9PMJk5eIqKKwvBOVEIkbuDtSPur7RR4OzPZnnmiALG0xmVjG7FywuS86iZEY/jAnHk8YZWIKgr/xSMqIXLGFDalpsPLa6QlUakwIvjGilVY1DAup+vHRiL4zlXrELXZ/UlElYXhnaiccNGdAkorBUdr/OPq9fhw0zxUO85Fr7OUwrrJ0/DA2g2YWlXDVXciqjhK2CxIVDIkbhC/s8N3CA//SQ2sywo7HIpoNIkIDLJ98E8dOYAX3zqGnkwaEdtGy5gG3Dq7GfXhCDxjYDG4E1EFYngnKjHJe8/A2+6j771WIfZ3DZw2Q2XDNea8lXUGdiIits0QlRTxBPb6iK9aZ42/OqJS1b8lhsGdiIjhnaikKEvBXhSCbspzE16Ngn19lKvuREREZY7hnajEiCeI3FMLNcXKrSCmEPkftVBVDO5EfhkRuMZwZjwRlTz2vBOVIPEEyADpB7rhvpQCBjhwVc9zEP5EFVSjxVV3ojz19tQfi3fjdx0nkfRcVDshLB03AdWOA4PsdBsiolLC8E5UosQIoAD0CDLPJeHtygAJA4QV9Awbzvoo9EQL4gmDO1EeRARKKfzq2Jt4bN9evHS8/bwBTyGtcc2UGfhg8zy0jKmHYoAnohLC8E4UAP0Dem+wZ6ggyo+IwBPBV17eimfePDTotQrAnQsX446WhSNzc0REOWB4JyKiivIXv9mMZ9sGD+59fW7REnyoaR40XywTUQnghlUiIqoIrjF4vu1wXsEdAO7b+SpOJBPczEpEJYHhnagMiQiDBlE/ttZ4bN/redcZCB7f9zoMn1NEVAIY3onKhHjZwO7ty8Dblob3chrewUz2Yy5DB9GR7i789sRbvmp/eqgVfBYRUSnI8yQYIio14gmQzk6kcZ9PQo6b8z6uJltw1kVgr4kANjiZhiqSZwx2nnzbd/2pVApvJ+KYVFVdxLsiIsofwztRgIknkHYPyb89AzllLn7NUQ/pH/Yg84sEIv9zDNCgGeCp4hgACW+AAxNyFHcLqyciKga2zRAFlHgC6TBI/E3ngMH9vOuPGyS/1gnpluxqPVEF0QBitlPQY1Q5hdUTERUDwztRQClLIXV/N9CVexCXDoP0g91ceaeKY2mNZeMn+j4xdVKsChOisSLfFRFR/hjeiQJIRGDaPZjdmbxrvVfSMGeGXqknKjfjolFcOXGKr9pbZjXD47QZIioBDO9EQSRA5pmEv1oPcJ9NsHWGKo5nDD7Y1JJ3XVhbuGVWM2zNX5lENPr4LxFRACmtYFr9b54z+1y2zlDFsbTG0vET8dHm+TnXKABfXXYFqmzOdyCi0sDwThRQkvK/ci5JrrpT5bpr0RJ8vGXhkNeFtMbXlq/CmsnTYHHVnYhKBJcSiAJKRZTvQ2NUlKvuVNnuXLgYN0yfjUf27cFThw+gp88YyMZoFDfPbMYfzW5GjROC9rnJlYhoOCjhGepEgSNGkH4kDvfn/vrendticG6IsnWGKpo5++svYzy09XQj4bqodkKYWl0DEWGPOxGVJIZ3ogASEchJg8Q9p5D38rsDxL7TAFXFYEJERBQ0/O1NFEBKKehxFqxF+R8aYy8PAzGuuBMREQURwztRQIknCH26Bqoh96exmmghdHtV/qv1REREVBIY3okCSlkKqloh8qUxUBOtIa/XMyxE/nIMEFZQmivvREREQcSed6KAE08AAdwXU3CfTcIcOH/+u55rw1kfhfWOEABwkyoREVGAMbxTWRNPAJ3tEQeyU1oAlOXKs7gCZSuY4x7ktAEAqLEaeqx17mNEREQUbAzvFGjinv/j2xtQxRMoS8HbnYa7JQXpNIBWUOM0nKsj0NNsBloiIiIKHIZ3CpzeYG46DbwXU9lVZp1dZbZXhoGogpw0SP1zN8yuzEUfQ8+xEbotBj3PKctVeCIiIipPDO8UKGIE5rCHzE/j8F5NA16/CxzAXhGGc2MUqkYjee8ZmH3uRR8LGgh9qhr2FeGSCPBiBFB9Wnwk28teCvdGREREpYHhnQJDjMB7KYXUP3VfGNr7iyhE7q6Bnmkj8dedkKMDFGgg8oVa6LnOqLXQ9LbveK0ZuJuSkI6z/er1GvaqCKy5Dlt8iIiICADDOwWEeAJvdwapb50BTI5FYSDy53UABMkvdw54mZ5jI/rluiLcZf5EBN6raWT+Iw5z+OIvMNQUC6FbYrCWhc6tyhMREVFl4px3CgRlKaQf7M49uANACkg/3ANrlgM92x7wMrPPhTninptEM1JEBO4vkkj9XdeAwR0ApM1D6r4uZJ5MgK+1iYiIKhvDO5U88QTe79OQ9nySe5bZnYE56sK+NjLodZnnk8AILmqLJ/BeTiP9UE/Op51mHo1nN+h6DPBERESViuGdSp6yFDIbk77rMy8kYS8PD/rTLifMiLakKEsh/Vg85+DeK/14nM9aIiKiCsYYQIFgBtpwmgM56kGFFBAbJJyPYMvMH95JyP9rkhMG3o4MV9+JiIgqFMM7BUPGf1iVs6PelTNweFd1I/dUUJZC5pcp3/XuL5NQFjeuEhERVSKGdwoEVe3/R1VVn52bHh+4Z96+Ijyiq9lysoB3EgqoJSIiomBjeKeSJ57AWhLyXW8vCcG0ucAAi91qogVrQWhkV7Pz33tbnFoiIiIKNIZ3KnnKUnDWRYCBpz0OrEbBWh4edMOr854oxB3ZHvJC2nTUGD5tiYiIKhVTAAVDlYJ9RTjvMufaKOAB7paLL7s7N0bhrIqM6Oml4gnslfl/Lb2sK8Ij/mKDiIiISgPDOwWDAKE7qgc9bKk/6/IQnJuiyPw8AST6hd1qhdDtVQi9v6rINzo0ZSlYS0P+Vt+rFewV4RF9sUFERESlw08jAtGIU1pBRBD5szFI/WMXvFfSg1wM2KvDCH28GuaYB++1NNRYDWhAjbNgrw7DXjH43PdhJ4Dz7ijS/9aTV5lzQ5QvuYmIiCqYEp63TgEiRqC0gjnqIvOLJNytKSB59ke4RsFZFYG9PgI91oKIAAYXbEQVV0pm5Tr1L91wczyAyl4VRvjTNcN8R0RERFTKGN4pkOTsoUpKK8jZGfDKUdm/VxjR01ILlX4ijswTCSA+wFMxquBsiCJ0cwwiEqivjYiIiIqL4Z1olImR7KbarSm4m5OQUwYQQNVr2FeGYV+ZnbSjNEM7ERFRpWN4JyoRF2vnKaUWHyIiIhp9DO9ERERERAHBuRVERERERAHB8E5EREREFBAM70REREREAcHwTkREREQUEAzvREREREQBwfBORERERBQQDO9ERERERAHB8E5EREREFBAM70REREREAcHwTkREREQUEAzvREREREQBwfBORERERBQQDO9ERERERAHB8E5EREREFBAM70REREREAcHwTkREREQUEAzvREREREQBwfBORERERBQQDO9ERERERAHB8E5EREREFBAM70REREREAcHwTkREREQUEAzvREREREQBwfBORERERBQQDO9ERERERAHB8E5EREREFBAM70REREREAcHwTkREREQUEAzvREREREQBwfBORERERBQQDO9ERERERAHB8E5EREREFBAM70REREREAcHwTkREREQUEP8f3sP7jd6euVwAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["score_clustering(got.Name, labels_pred)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1rp-Mc8NV6Ms","executionInfo":{"status":"ok","timestamp":1700671418332,"user_tz":-60,"elapsed":220,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"260ab450-1e5c-449b-ffcb-557a72bf6b67"},"execution_count":97,"outputs":[{"output_type":"stream","name":"stdout","text":["Rand index : 0.9941176470588236\n","Adjusted Mutual Info : 0.9639919485176365\n","Homogeneity : 0.9712364486661813\n","Completeness : 0.9672494819839255\n","V measure : 0.9692388652428515\n","Fowlkes Mallows : 0.9651796981542199\n"]}]},{"cell_type":"code","source":["from yellowbrick.cluster import KElbowVisualizer\n","# See https://www.scikit-yb.org/en/latest/api/cluster/elbow.html"],"metadata":{"id":"lGz-NqVDWHVh","executionInfo":{"status":"ok","timestamp":1700671451702,"user_tz":-60,"elapsed":257,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":98,"outputs":[]},{"cell_type":"code","source":["%%time\n","model = KMeans(random_state=1)\n","visualizer = KElbowVisualizer(model, k=(2,20), metric='silhouette')\n","\n","visualizer.fit(X.toarray())\n","visualizer.show()\n","# This will take a while"],"metadata":{"id":"W1QeblrIWJAC","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1700671570170,"user_tz":-60,"elapsed":55010,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"da25b617-52d5-48ee-c6fb-ac9b9dd3e746"},"execution_count":99,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n","/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n"]},{"output_type":"display_data","data":{"text/plain":["
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6aV8/fXXTJ8+vV7iXDfm5l7PGRkZXH/99YwZM4bnnnuO+Ph4FAqFrxa7rmO7xHgnqnpf188//zyfffYZ3333HYsXL0atVnP22Wfz8MMPNzhuXomJiRw6dOg4R+jEyGQy3nvvPT766CN+/PFH3nnnHUJDQ5kzZw733HNPk8cN4LHHHmu0JK3u329Tz+tYTR07wPdF6Ni/5c42gV4QOpJI0AWhDbyTs45tpzdkyBDuvfdebrvtNg4ePOiX7hjeZKimpqbeqXRvAls3WTp2pK7uJC3vtgkJCY0mSHUT7eZ4R09ffPHFRkfXjvfBW11d3SDBk8vlzJw5k61bt7Jo0SIkSfLto7q6utEk3Zs41p0s6WUymY47qulNOq699lrmzJnT4Pb2bOd37BcH788hISEtfk7jxo0DPL34169f7yvdGTNmDBs3biQ5ORmZTObbriVa8zprjbi4OMAzEv/II4/w1FNPMXDgQN+8hsbMmjWLe++9l9WrV5ORkcF9993XZMzNvZ5/+OEH5HI5CxYs8B1ft9vtG91tDZVKxVVXXcVVV12F0Whk5cqVvPDCCzidTv7zn/80eh9vXXZVVVWjZx0cDgdLlizhwgsvbLJUprm/65CQEG699VZuvfVWSkpK+O6773j11VfRarX1SqW8vHE88MADTJkypcHtLU3KW8r7JcFisdS7/tgzGIIgHCVKXAShlUpKShgzZgxvvfVWo7d7OxI0VYPdWt4Sj82bN9e7fsuWLcjlcl89bFhYGBUVFfW22b59e72fR4wYQXZ2NomJifTq1cv3T5IkXyLl1dhpee91Q4cORaFQUFBQUG8/sbGxyOXyJj/gV6xYwdixY1m3bl2jt+fn5xMbG4tMJvOVfhy7eNEtt9zCxx9/jMFgoF+/fr5SD6+SkhLy8vIanE6vKyQkhAEDBpCdnV0v/l69emG329u17eCx8XrLCfr169fi5xQSEsLIkSPZuHEjGzdu9CXiY8aMYdOmTWzevJmxY8cetzb6WC19nZ2Iyy+/nPPPP5/nnnuuQaeWuqZNm0ZISAjz588nLi6u0ZaM0LLXs8PhQK1W1zsrsXz5cqxWa6tKT4xGI998842vY0tERARz5szhvPPOY+/evU3e76qrrsLlcvHcc881evtrr73Gs88+y/79+xu9vbm/6+LiYpYvX+77OS4ujhtuuIFJkyY1iMv7fHv37k1YWBh5eXn1jluPHj1wOp1+H9lOTU0FqNcRR5IkVqxY4dfHEYSuRCTogtBKcXFxXHHFFbz99ts899xzbN++ncOHD5OZmcnChQt5+eWXmTVrFikpKX55vGHDhjFhwgTmz5/P77//Tl5eHt988w1vv/02s2fP9iUiw4YNIz8/n88//5y8vDyWLl3aoGf01VdfTW1tLffddx8ZGRnk5eXx+eefM3v2bN8KqN7RtY0bN5KZmYnVavWNnq5atYqDBw8SExPDRRddxBtvvMGyZcvIy8tjx44d3HnnnVx55ZUNRsq8vDXAd911Fx988AF79+6loKCAbdu28cQTT/DLL7/4ukgMGzaM8ePH88ILL7BhwwZyc3N5/vnnWbNmja+O+8Ybb2T16tW88cYb5OTksH37du666y4iIiLqtVNszM0338wvv/zC66+/zoEDB9i/fz/PP/88559/frM16G63m9LS0ib/Wa3WJu9rMpl47rnnOHDgAOvWreO1115jyJAhvjMRLX1O3jIQm83mm8MwcuRIKioq+P7771tVfw4tf52dqKeeeooBAwZw1113NblqqFarZcaMGezfv59zzjmnybM7LXk9jxgxgtraWj744APy8/NZunQpixcvZsSIEezbt6/FLf4kSeLJJ5/kscceIzMzk8LCQv766y9+/fXX456p6Nu3L0899RTffvstN998M+vWrePw4cNs27aNf/7zn7z77rs89thjTX6hHDZsGL/++ivr168nOzubl156qV7CXl1dzX333cdLL73E/v37KSwsZNWqVWzdutUXl/dv+o8//iArKwulUsk//vEPPv30Uz766CNycnLYu3cvDz/8MHPmzPF7q9G0tDT69OnDwoULWbduHQcOHGDevHm43W6/Po4gdCWixEUQ2uChhx5iyJAhfPnll/zwww9UVlai1Wrp378/Dz74IJdccolfH+/NN9/kP//5D48++ihGo5H4+HiuvPLKei3RrrrqKvbt28eLL76I0+lk8uTJPPbYY1xxxRW+bXr16sXHH3/Myy+/zNVXX43D4SA1NZUHH3yQyy67DPBMSLz88sv56quv+P3331m2bBnnnXce3333HXfffTdTp07ljTfe4IknniAuLo7XX3+doqIiQkJCmDx5MosWLWpy5FatVvP+++/z8ccf8+233/LWW29hMpkICwtj2LBhvPvuu/XaAr7xxhu88MIL3H333VgsFvr3788777zDkCFDAJg9ezZut5v333+ft99+G61Wy7hx4/j3v//d7CigN/FbuHAh77zzDkqlkvT0dN599916E0obU1FRcdwE+LnnnmuyQ8XFF1+MzWbjqquuoqamhtGjR9cr0Wjpc5o8eTIvv/wy48aN85UQhISEMGjQIHbt2tVoe8XmtOR1dqI0Gg2vv/46F154IXPnzm3Qj9zrvPPO8/WDb0pLXs9nn302GRkZvPPOO74Fkl555RW2bNnCY489xrXXXsuqVauajTsyMpL333+fV199lauuugqr1UpCQgIzZ85stIykrgsvvJC0tDTee+89HnjgAYxGIzExMQwbNoxPP/203iTxYz322GM8/vjj3Hrrreh0Oi688EKuvvpq3+rE/fv35+233+att95i8eLFuFwukpOTuf76630Tlk855RRGjRrF/PnzGTBgAEuXLuXmm28mJCSExYsX85///Ae1Ws3YsWNZvHix387+eclkMt544w3mzZvHTTfdRGRkJJdccgmXXnop//rXvzrFyruC0NFkUntOLxcEQRAEoduzWCzY7fZ6dfj//ve/+fLLL/26UrEgdBViBF0QBEEQhHZ1/fXXU1ZWxjPPPEOPHj3Yvn07X375pa93uiAI9YkRdEEQBEEQ2lVZWRn/+c9/+Ouvv6iuriYxMZGzzjqLW265RZS4CEIjRIIuCIIgCIIgCEFEdHERBEEQBEEQhCAiEnRBEARBEARBCCIiQRcEQRAEQRCEICK6uOBZKU8QBEEQBEEIfqNHjw50CO1OjKALgiAIgiAIQhARI+h1dKZvZGazmb179zJo0CD0en2gw+nUxLH0H3Es/UccS/8Rx9I/xHH0H3Es26Y7VTyIBF0QBKEOk8lEZmYmAAMHDsRgMAQ4IkEQBKG7ESUugiAIdWRmZjJ9+nSmT5/uS9QFQRAEoSOJBF0QBEEQBEEQgohI0AVBEARBEAQhiIgEXRAEQRAEQRCCiEjQBUEQBEEQBCGIiARdEARBEARBEIKISNAFQRAEQRAEIYiIBF0QBEEQBEEQgohYqEgQBKGOkJAQxowZ47ssCIIgCB1NJOiCIAh1DBo0iBUrVgQ6DEGo56mfdwAwb8bwAEciCEJHEAm6IAiCIASxp37ewdMrdvp+Fkm6IHR9IkEXBEEQhCB1bHLuvdxdk3RJkqg0F2Fz1KJRhRCpT0AmkwU6LEHwO5GgC4Ig1FFRUcGff/4JwJQpU4iKigpwREJ3dWxy7tVdk/TiqmyyijZgtlf7rtOrw0hLGE98eO8ARiYI/icSdEEQhDoOHjzI9ddfD8CKFStEgi4ERFPJuVd3S9KLq7LZlruywfVmezXbclcyMuUMkaQLXYposygIgiAIQaS55Nzr6RU7fZNHuzJJksgq2nDcbbKKNiBJUgdFJAjtTyTogiAIghAkWpqce3WHJL3SXOQra3G5nZisldidtnrbmO3VVJqLAhGeILQLkaALgiAIghC0bI5a3+VamxG704LJWt7IduaODEsQ2pVI0AVBEAQhSMybMZwnpg9r8fZPTB/W5evQNaqjC4Y5XXbf5WNLWjQqfYfFJAjtTSTogiAIghBEWpqkP35GepdPzgEi9Qno1WFwTIm5y+3wXdarw4jUJ3RwZILQfkSCLgiCIAhBZt6M4ZzaN/6422iUig6KJrBkMhlpCeNxS65617vq/JyWMF70Qxe6FJGgC4IgCEKQ+W1/EX8eLGn0tjiDFoDHftzOFzsOdWRYARMf3pu+cSORyz3docN0MWiUOvTqMNFiUeiSRB90QRCEOiIjI5k9e7bvsiB0tKJqC1csWo1bkojUqblqTB9eW50JeGrOrxvXjwmvLqe4xsq1n6wlNTKEsSkxAY66/cWFpdI/fiyVtYUMSjoJnTpUrCQqdFkiQRcEQaijb9++/O9//wt0GEI35XK7uXLxaoprrAC8f9lJnDukJxE6NXB0YaJl109l6psrsDpdzP7f76y760xSIkOa3G9XEBmSwOgQUWcudA+ixEUQBEEQgsS/VmTw2/5iAO47dTDnDukJeBLzuhNCx6XE8OHlkwAoqrEw673fqLE6Gu6wi3K5nVRbynG5nYEORRDahUjQBUEQBCEIrMwq4JlVnkWKTkqN5d9njTzu9hcN78UzZ44AYGdhJZcvWo3L7W7vMAOuylzKqt3v89f+r6i2lAU6HEFoFyJBFwRBqKOoqIjXX3+d119/naIisTKh0DEKqsxc9ckaJAmi9Ro+ufJkVIrmP6IfmjaUq8f0AWD53sM88N2W9g41IKyOWjIL15NfmYVSoUY60nPRZKsMcGSC0D5EDbogCEId+fn5zJs3D4CJEyeSkCBqXoX25XS5uWLRakpNnuXrP7x8Ej1bWE8uk8l4e84EsitMrD5Ywqt/ZjIgNpxbThrQniF3uGpLOTllnrMLk/pfhEapx+Y0Y7JWBDgyQWgfYgRdEARBEALoyZ93+FoqPjRtKGcOSm7V/TVKBV9ecwp9o0MBuPPrjazIKvB7nIHkHSmXISdEE45BGwVAjVWMoAtdk0jQBUEQBCFAfso8zHO/7AJgSp84nmrjyqAxBi3f3jCVCJ0al1viko/+ZE+R0Y+RBpZ3pFyvCUMuUxCqjax3vSB0NSJBFwRBEIQAyDfWcvXitQDEGjQsvvJklC2oO2/KwPhwPr96Ckq5jGqrg/Pe+41Sk9Vf4QaUdwTdoIms97/dZcXmtAQsLkFoLyJBFwRBEIQO5nC5ufzj1ZSbbchk8PHlk0kK15/wfqcNSOSNC8cDkF1h4sL3f8fmdJ3wfgNJkiRMViMAhiMj594SFwCTKHMRuqCAJuiHDx/mpptuYvz48UydOpUXXngBdxMtompra7n//vtJS0vjwIED9W7Lz89n7ty5jBs3jvHjx3PjjTeSnZ3dEU9BEARBEFrtseXbWJtTCsCjp6dzRlqS3/Z944T+3HvKYADW5pRy4+frkCTJb/vvaBZ7DW7J0+/86Ah6hO92UeYidEUBTdDvuOMO4uPjWbVqFe+//z6rVq3iww8/bLBdcXExF1xwAQqFotH93HbbbcTExPDbb7/xyy+/YDAYuOeee9o7fEEQBEFote/35PPi73sAmNovniemD/P7Y8w/ZyTnDukBwOIt2fx7VYbfH6Oj1G2lGHpk5FypUBOqjSJCH49KqQlUaILQbgKWoGdkZJCZmcn9999PaGgoqampXHvttSxZsqTBtpWVlTzwwAPccccdDW6z2+1ceeWV3HfffYSEhGAwGDjnnHPYv39/px4xEARBELqeQxUmrv3EU3ceH6pl0RUno5D7/6NYIZez6IrJjEjyjDjP+2kHS7bl+P1xOkLNkRFymUyOXhPmu35S/4uY0HcWSRH9AxWaILSbgPVB3717N8nJyYSHh/uuGzJkCNnZ2ZhMJgwGg+/6gQMHMnDgQPLz8xvsR61WM2fOHN/PhYWFfPLJJ8ycOROZTNa+T0IQhC4nISGB22+/3XdZEPzF7nRx2cerqbTYkctkLLpiMglhunZ7PINGxTc3TGXCqz9SWG3hus/W0isqhAm9YtvtMdtDfFgqKoUah8uGXNb4mXRB6GoClqAbjUbCwsLqXedN1isrK+sl6C01dOhQHA4HZ5xxBk8//XSr7282m1t9n0CxWCz1/hfaThxL/+kKxzIqKoqHHnrI93Og3he6wrEMFsFyLB/6cQcbcj1L0z88dRATksPb/fUVpZax5PKJzHjvDywOF7Pf+43fb5lKSkTLFkKqK1DHUY6GGF1voHN9Th9PsLwmheAV0JVE/V2CsmvXLoqKinj++ee54YYbWLx4MfJWnDrcu3evX+PpCDk5OYEOocsQx9J/xLH0H3Es/SeQx/L3vGre/MtzFnhcQgjnxEkd9pmjBZ6ckMiDq/MprbVx3ru/sXB6KgZV20ajg+U1KUluLFIldnctenkUannrB/YCLViOpRB8ApagR0VFYTQa611nNBqRyWRERUU1fqcWSEhI4OGHH+bkk09m9+7dpKent/i+gwYNavPjdjSLxUJOTg6pqanodO13irQ7EMfSf8Sx9B9xLP0n0Mcyp7KWfy/9BYCEUC2fXXMqsQZth8YwaBBYtRHMW7mLA1U25m83suSKk1rVdz3Qx/FYLreT1fs/QUKiR2xPekaKz/CurjMOpLZVwBL0oUOHUlhYSEVFhS8hz8jIoF+/foSEtPzU28GDB7nuuutYtmwZkZGeyTDeUXOVStWqmPT6E+9B29F0Ol2njDsYiWPpP535WObm5rJgwQIA5s6dS0pKSkDj6czHMtgE4ljanC6u++J3qqwO5DIZn1w1hV5xbR+EOhGPzhhBTpWF9zceYMW+Yh5ftYdXzx/X6v105HEsqT7EvuJNGDSRDE6a3KBji0EXgdlejV2q7ZR/J+Lvu+NsPPgdewrWcu3k+Q1u23ZoJTvzfkV2zByHOWMfRKcO7agQ6wlYF5fBgweTnp7OSy+9hMlk4sCBA7z//vtcdtllAMycOZPNmzc3u59evXoRGhrKM888Q3V1NSaTiZdeeomUlBT69OnT3k9DEIQupqSkhP/+97/897//paSkJNDhCJ3cP7/bwua8cgCenjmcU/rGBywWmUzGggvHc+qRGN5Yk8WCNVkBi6claqzl1FgrKKrORqFoOOjmXbBI9EIXjqfcVMCBkm3H3aZP3EiunvRMvX+BSs4hwH3QX3vtNUpKSpg0aRJXX301s2fP5vLLLwcgOzvbNxlkwYIFpKenM3PmTABmzZpFeno6CxYsQKFQ8M4772A2mznllFOYNm0aZWVlvP3226jV6oA9N0EQBKF7+3LHId44kgDPGJjEg6cNDXBEoFYq+OLaU+gf40k87lq2iZ8yDwc4qqZ5Vwk1aCKQyxqmLKFHFi4y2YyitbLQKElys+7A1wxOnhzoUFoloJNEExISWLhwYaO3ZWUd/VY/d+5c5s6d2+R+kpOTeeutt/wenyAIgiC0xYGyGm78fB0AyeF6PrpsEnJ5cLT+jdJr+PYfp3HSqz9SabFz6UerWXPHDIYmRgY6tAa8ixR5VxA9lkHrud7ldmBxmNAHcMRTCE5ZRRtRyJT0iR3BtkMrmtyusraIH3YswGguJkQTztje55AcOaADI60voCPogiAIgtDVWB0uLvnoT6qtDhRyGZ9edTIxHTwptDkDYsP44tpTUMpl1NgcnPfeb5TUBFfLP7fkxmQzAkdLWY5V93pR5iIcy2KvYXvuSib2m33c7UI04YRqozl5wMVcMu5R+seP5Zc9H1JlLu2YQBshEnRBEARB8KN7v9nMtsOeZPHZs0YyqXdcgCNq3NR+Cbx10QQADlXWcsH7f2B1uAIc1VFmWzWS5AaOjpQfK0QTjuxIKuMdbRcEr03ZP9AvbgwR+uPP/RiQMI6pg64gTBeDUqFmSPLJRIUkcaD0+HXr7Ukk6IIgCILgJ59uzeaddX8DcPbgZO49ZXCAIzq+68f344GpQwBYd6iUG5b8FTS13Cbb0RHxpkpc5DIFeo1n0UNvvbogABQY91NSfYjhKdPadH+DNhKLvdrPUbVcQGvQBUEQBKGryCqp4pYv1wOQEhnCB0FUd348z541kn1l1SzLyOOzbTkMiA1j3ozhgQ7Ll3DLZYrj1pb3jBqI0+UgMiSho0ITOoGDJduwOEx8ucnTVlHC88Xz0/VPM77vLPrEHn2N78j9hbiwXiRG9PNdV2UuITV2WMcGXYdI0AVBEAThBFkcTi79aDUmmxPlkbrzKL2m+TsGAblcxkeXTeLUyhVsza/g6RU76R8bxuWjegc0rlpv/bkmElkjHVy8UmMCl0QJwWtsn7MZ2Wu67+daWxXLdy7gvJF3oVHqWLrlJSb1u5D48FRsTjPrDnzDtEFXE6KNILNgHdXWcvrFjQ5Y/CJBFwRBqKNnz548//zzvsuC0BJ3fb2JnYWeEd/nzxnFhF6xAY6odUI0Kr65fioTXv2Rw1VmbvjsL1IjQzgpgPXzw3pOpX/8WJxue8BiEDovjVKPpk6W65Y88ytCNOEAVFtKcbhtAIxK9bTx/nnXQmxOMxH6eGYM/Ydv20AQCbogCEId8fHx3HjjjYEOQ+hEFm05yHsb9gMwa2hP7prSeZacryspXM83109lyps/Yba7uOCD31l355n0jg5M60KZTO6rLxeEExWqjaq3imjdy0q5inF9zmVcn3MDEVqjxCRRQRAEQWijvcVV3Hqk7jw1KoT3LpmITBb8dedNGdkjikVXnIxMBqUmG+e99xtVFs8I9rO/7uG/O4Nzdd2/izayKfsHDpXtCnQoguAXIkEXBEEQhDaotTm45KM/MNtdqBRyPrtqCpGdpO78eGYN7cnzZ48CYE9xFZd89CfzftzOc7/t5d1dZTz76552j8HmtGBzmFvcUaa0Jo9y02EqzUXtHJkgdAyRoAuCINSxf/9+rrzySq688kr2798f6HCEIHbH15vYXVQFwIvnjmZsSkyAI/Kfe08dzA3jPR0tVv5dyDOrMny3PffbXp76eUe7Pn5u+W5+y1zEH1mftihJ9/ZJF60Wha5CJOiCIAh1GI1Gli9fzvLlyzEajYEORwhSH2w8wIebDgBw4bAUbpucFuCI/Esmk/HmheNJjQxp9PanV+xs1yTdm2irldoWlQyFajwritbaqnyTAQWhMxMJuiAISJJERW0hhcb9VNQWBs1CJYIQDJ76eUe9ZHRXYSW3L90AQN/oUBZe3Lnrzpvy7KoMciprm7y9PZN076qghiOJd3O8I+gSbmptVe0SkyB0JNHFRRC6ueKqbLKKNmCus2KaXh1GWsJ44sMD2wdZEALtqZ938PSKnb6f7zt1MJd89CcWhwu1Qs5nV59MuE4dwAjbx7HPuynebfy5sJHb7cJ8JMn2Jt7NqbudyVpJqLZlib0gBCsxgi4I3VhxVTbbclfWS84BzPZqtuWupLgqO0CRCULgHZukPr1iJ5Ne+4nMEs/fy//NHsOoHtGBCq/dtDQ59/L3SHqt3ehb9TG0hQm6ThWKXOYZc/SOvgtCZyYSdEHopiRJIqtog+9nh8uOxW6qV96SVbRBlLsI3VJTSequIiMAl4xI5ZaJAzo4qu6hps5ET4OmZQm6TCbzJfMma0W7xCUIHUkk6ILQTVWai46OnEueDzWLvRqLvca3jdleLdqWCd1OS0aQU6NCumTdOXjKVZ6YPqzF2z8xfZhfS1y8E0QVchValaHF9/OWudSITi5CFyBq0AWhm7I5jk7+cksuJMkNgNVhQq8OA5l3O3MgwhOEgGhpecfzv+5Go1T4NTENJt7n1dyxePyMdL8fg6MTRCNa9SUoNSadHpGDWly3LgjBTCTogtBNaVRH26cd25bM4bajUqiPbKfv0LgEoS5vbXNHJMJtqb2GjoktEFqSpA9N9H8yrFHq0alDWz3RM1Tb9eYDCN2XSNAFoZuK1CegV4dhtlcjlynQqkOxHilvsTnMqBRq9OowIvUJAY60Y/Xr14/Fixf7LguBc2zC7M9EWJIkCqot7C2uIqukir3FVfy497Df9t9VNJWkh2tVVFkd3L1sE2cMSPRrJ5shyZMBxPwXoVsTCbogdFMymYy0hPFsy12JXK5Arw5FklzYHGbsLguSFE5awvguW2fblIiICM4888xAh9HtNdZBBVqfpDtdbvaV1fBHfg3LS7M4UGkmq6SKzJJqqq2OE4rR37XXwerYJP3hqYM4qW8i5777K4XVFh7/cTuvXTDO74/b1vcet+TC4bKjUer8HJEgdByRoAtCNxYf3puRKWf4+qBrlHpsDjNymYIeUQNFH3QhIJoqNTlekl5rc5BVWn1kRLyavSVVZBZXsa+sBofLfWSrvCYfs2eEnoFx4QyKD2d/WQ3LmxlN7y7Jude8GcNxOByUlpbyyGmD0ev1XDgsha925rLgryyuHNOHcSkxgQ6THXm/UmQ8SExoD0anzgx0OILQZiJBF4RuLj68N3FhqVSai7Daaykw7qNn1CBiw3oGOjShG2quDvzpFTs5VGFiQmosmSVV7C2uJrOkitzjrHjppZTL6B8bdiQRDyMtLpxBceGkxYVh0KhaHEd3S869HjltMHv37vX9/MrssazIKqTG5uDWL9az4e6zUCpOrDlcaU0edqeFMF10m2rKFTIlEm5fJxhB6KxEgi4IAhn5vyOXyYkP782Y3t27vGPPnj3MnTsXgAULFjB48OAAR9R9tHSS5oebD/Lh5oNN3m7QKBkUF87A+HAGxoXRO1yLqqaUaWOGER7asrZ9TdVed9fkvDFJ4Xr+fdYI7vx6E9sLKnl9TSb3nHJify955XsoqTlEhD6eCX1ntfr+3omlFkcNTpcDpULVzD0EITiJBF0QujlJkiisOoAkudGpw4gNTQl0SAFlNpvZuXOn77LQMVrbQQUgRK1kXEq0byTcm5Anh+vr1S+bzWb27q1G1crR3WOTdJGcN3TLSQP4ePNBNuWV88RP27lwWC9SIkOav2MTamyeRYZaukDRseq2WDTZKonQx7U5FkEIJJGgC0I3Z3OafT3QdepQ3/VuyU25KZ9oQw/kMrGmmeB/brfEjoJKVmQV8OGmA62+/32nDm73hLnu/kVy3pBCLuftORMY98pyzHYXdyzdyLLrT23TBE+X2+lbKK2tvcwNdVozmqwiQRc6L5GgC0I3Z7GbfJd1Kk+CbrJWsin7e2xOC6N6zSQurHuPqgv+U1JjYcXfhazIKmBlViElJmub9tORo9kiMT++EclR3HnyQF7+Yy/f78ln2a48zk9v/XuGd4EiaPsIukapQ63QYndZMR0ZjReEzkgk6ILQzVkcNb7LOrWnPlevCfP1ID5cmSkS9G7KH4sEOVxu/sopZUVWASuyCtia3zBpkstkjEuJZnpaEvnGWv638fij6aLUJPg8OWM4X+44RJ7RzF1fb+L0/omEaltX/113YueJrAZq0EZSUVtIjZgoKnRiIkEXhG7Oe0pZLlOgUep9l5MiB5BTtpOS6lxsTovoKdzNnMgiQQfLa/g5q4AVmQX8tr+YGlvDfuPJ4XqmpyUyY2Ay0/onEKXX+G7rEREiOqh0MgaNitfOH8f57//O4SozT/y0nZdnj23VPrwJukqh8b0XtSkWbRQVtYWik4vQqYkEXRC6OW+CrlUZ6tWN9ohMI6dsJxJuCo37SY1JD1SIQgdr7SJBJpuD3w8UsyKzgJ+zCthfVtNgG41SzpQ+8cwYmMT0tCQGx4c3WacsOqh0TucN7cns9J4sy8jjjTVZXDm6D6N7trxVorfExaCJPKEF0kI1kSjkKrSqENxuF3K5os37aoo/zi4JwvGIBF0QujlviYu+zgRR8JwmDtfFUWUp4XBlFr2ih3a7VUW7o5YsEiRJEjsLK/k501O2sia7tM5iQEcNig9neloi09OSmNInHr265R85ooNK5/Tq7LGs+rsQk83JrV+uZ91dZ6KQt2ySuXfE+0TKWwCSo9LoETWo3d6vTuTskiC0lEjQBaGb846g645J0AF6RKVRdbiEGmsF1ZYywvWxHR2e0IFaskjQsoxcSkw2imosDW4P16qYNsCTkM9ISzqhdnsgOqh0Rj0iQvjXzBHc881mtuRXsGBtFnecPKjZ+0mSRO/YYdRYK4gx9DihGOQy/4+Ye7X27JIgtJVI0AWhmxuRcjpmezVaVcMFXBLC+7K3YB1uyUl+ZVa3SNAHDRrEH3/8AUCfPn0CHE3HaWkf8p2FRt9lmQzG9oxmRloy09MSGZcSc8IrSR5LJD6dz22T0/h4y0G25lfw2I/bOT89hR4Rx/+yJpPJSIke0kERtk1Lzi4Jgr+IBF0QurkwXQxhuphGb1Mp1CSE96bAuI9C434GJk5AIe/abxshISGkp3evevu2LBJ0QXoKb8+ZQHSIpvmN20iSJCrNRdgctWhUIUTqE0SZVSegkMt5+6IJTHj1R0w2J3ct28RX157aoTHYnGZqLOU4XHYSI/qe8P5acnYJRJIu+E/X/qQVBOGE9YwahEqhJjkyrcsn50LLDU2MaNfkvLgqm6yiDZjt1b7r9Oow0hLGEx/eu90eV/CP0T2juX1yGq+tzmRZRh7f7srjvKE9O+zxs0t3kFOWgVqhPeEEvaVfYEWSLviTWB5QEITjigxJYFDSpCZH2YXOb96M4TwxfViLt2/vCZvFVdlsy11ZLzkHMNur2Za7kuKq7HZ7bMF/np45guRwT7vEO7/eiKmRdpteewvWsiPvVwqM+/3y2AaNZ0VRu8uKzdlwvkRLtfbs0tMrdvo6vAjCiRAJuiB0YwdLtvN75idszl4e6FCCxvbt2+nXrx/9+vVj+/btgQ6nw8ybMZyHpw1tdrv2Ts4lSSKraIPvZ4vd5FntVjq6TVbRBt9CWkLwCtWqePV8Ty/0PKOZp35uOtEtrj5EoXE/lbVFfnnsup1gTNaOXVG0sY5GgtBaIkEXhG7MbK/G6jBhcZhatL3DacNoLmnnqALL6XRSUVFBRUUFTqcz0OF0GEmSGu1fXldHtDqsNBf5Rs6dLjsWezUWezUOl823jdleTaXZP4mc0L5mD+3JOYM9XVleXb2X7YcbJstOlx3rkfegE22x6GXQ1E3Q275gUWvPLoHneV74we98vPkglWZb83cQhEaIBF0QujFvD3SdqmGLxWMdLN3Ob5mL2J67SoxedkHPrsrgix2HAE//8mN1VB9ym6PWd9nhste5bDtmO3O7xyKcOJlMxusXjCNErcTllrj1y/W43PVHmL0LFEH9xPpEKBUq3/ta3f23xbwZw5meltjsdnqVp72j2e5iWUYe1366loR5XzD97ZW8tTaLgirxmhVaTiTogtCNHe2B3rDF4rF0KgNuyYXVYaKitqC9QxM60NcZuTzxk6dudmRyFBvvPqveqGFHLhKkUR1tx+dyOxq97Nmu7UvBCx0rJTKEJ4+8fjbmlvPOX/vq3V5TZ4TbXyPodfdVc4IlLs+uymBFVuFxt3li+jCMz17Kb3Onc9eUgfQ6sgaA0y3xy74ibl+6kZ5Pf8VJr/7If37dxb5mzlYJgmjJIAjdlCRJvtKWxhYpOlZcWCpKuRqn205+RSbRhuT2DlHoADsLKrnmk7UAxIdq+fq6U9GrlQFbJChSn4BeHYbZXo1OHYrDZUOS3DjdDpAkkMnQq8OI1Cd0WEzCibvz5IEs2nKQHQWVPLJ8G7PTe5J0ZAKptwRFrdCiUer89pgGbSSlNbmYrJVIktTqFp2SJPHYj9uZ/8suAHpHGThncDKvr8mqt13dL7BT+sYzpW88L503hu2HK1m2K5dlGXnsKjICsCG3jA25ZTwM9A7XcNFhN3NG9mFUj6hWx+edjCq6xnRNIkEXhG7K5qxFkjynmltS4qKQK0mM6EdexR6Kq3NwuGyoFO3XZk9of6UmK7P/9xu1didqhZyvrj2VnnVW/wzEB79MJiMtYTzbcleikCsJ0URgslYgSW5ckhOFTEVawnjRD72TUSrkvD1nAie99iM1Ngf3fLOZJVdPAY6WoBi0UX59zNAjnVycbjs2pxmtquUr27rdEvd8s4k3jiTjabFhrLjldHpEhBCp1/g6uzR1dkkmkzGyRxQje0Tx1MwR7C+rZllGHssy8lh3qBSA7CobL/yRyQt/ZJISGcKsoT2ZPbQnk3vHNbvg17HdZUSS3vWIBF0QuimL/ejE0JaMoAP0iEojr2IPbslFofEAKdGD2ys8oZ3ZnS7mfPgHhyo9Nd9vz5nAxNTgWCk2Prw3I1POIKtoA27J5bteLlMyMuUM0Qe9kxqXEsOtJ6WxYG0WX+44xPK9hzlrULKvy4o/y1vAswhbfFgqBm0UMlr+hc7ldnPzF+t5f+MBAIYlRvLTzdOID/WM7rfl7FK/mDDunzqE+6cOobDazJdbD/Lppr/ZUmLG6ZbIrazl9dWZvL46k2i9hnOH9GB2ek/OGJCE9khtu9exybnov941iQRdELopb/05tKwGHSBMG0OoNooaawWHK7NEgt5JSZLEHV9vZPVBT0eee04ZxDVjT3y1RX+KCe1JXFgqleYi9hasJUQTQe+YEYTrRT/+zuyZM0fwdUYuhdUWbv9qA9vunY7N6Zk86a8Jol4GbSQje01v1X0cLjfXfLKWJdtzABiXEs0PN04jSl//bOGJJMOJYXpuGNeHk0JtJKb25becCpbtyuOnzMOY7S7KzTY+2HSADzYdIEStZObAJGanp3D2oGRe+XNvo33ZRZLe9YgEXRC6KavTM3IqlylQK1pW9ymTyUiOTCOzcB1VllJqrBWE+vm0tND+3lyTxbvrPQvCzBiYxPPnjApwRA1ty12JyVpJUkR/JvW/KNDhCH4SrlPz8uyxXPrRnxyqrOW5X7N4/IxLMFkrAr4YmtXh4tKP/+S73fkATOkTxzc3TCVMq263x4zQqblidB+uGN0Hi8PJyqxClu3K47vdeVSY7dTanXy1M5evduYil4H7OA20RJLetYgEXRC6qT6xI+gZNQibw9yqet7EiH5HFopxc7gyi4GJE9sxyo43YsQIDhzwnNoODW1Z6U9nsurvQu79djPgqav95MqTUciDq6GXJElUmUtwuGy43Pbm7yB0KhcNS+HMQcn8uPcwL/+xlytG9yE9MbBlS2a7k/Pf/51Vf3u6tZwxIJGl13kmTHcUnUrJeUN7ct7QnjhdblZnl7AsI5dvduWRZzQfNzn3Ekl61xHQd+XDhw9z0003MX78eKZOncoLL7yA2934Cly1tbXcf//9pKWl+T48vSorK3nwwQeZNGkS48eP5/bbb6ew8PgtkQRBAJVC0+q6T41SR2p0OgMTJ9A7dkT7BBZASqWSyMhIIiMjUSq71hjGvtJqLv3oT1xuiQidmmU3TCVC136jg21lcdT4+p6H6+MDHI3gbzKZjDcuGIdOpcDplrjli/W4W5J9tkG1pYzMwnVsyfmpyfUbqq12zlr4iy85P29ID765YWqHJufHUirkTO2XwKvnj+PaVpafPb1ip6/Di9B5BTRBv+OOO4iPj2fVqlW8//77rFq1ig8//LDBdsXFxVxwwQUoFIpG9gIPP/wwZWVlfPfdd/z88884HA4efvjh9g5fELqttMTxpMYM82tLNKF9VVnszP7fb1Ra7MhlMj696mQGxIYFOqxGVdVZrTZc55m4mlO2k00HvyercEOgwhL8KDXKwLzpw1Er3Kw/VMZ/1+9r/k5tUGurIqcsg9KaXN/CbHVVmG2c8fYq33yMy0am8vk1p6BRNp5vBILoWNQ9BSxBz8jIIDMzk/vvv5/Q0FBSU1O59tprWbJkSYNtKysreeCBB7jjjjsa3CZJEvHx8Tz44INERUURERHBpZdeypYtW8Rqh4LQBEmSqLVV4Xa7mt+4m3G5XNTW1lJbW4vL1TWOj8vt5vJFq8ksqQbgpfNGMz0tKcBRNa3K4mlDp1Jo0Ks9XyIqagspry2gzJQXyNAEP7r7lEHcO6mE28bn8unmXymqtvj9MeqeITRZ668oWlxj4bQFK9icVw7ADeP78eHlk1A10+Kwo82bMbzewmHN6ciFxYT2E7DzN7t37yY5OZnw8KNLSg8ZMoTs7GxMJhMGw9GuEgMHDmTgwIHk5+c32I9MJuOpp56qd11hYSGxsbGt/tZpNneeZXgtFku9/4W2647H0uqoZV32lwAMTZpKrCGlzfuqtRlRK3WoFJoucSy3bt3KeeedB8C3337LqFGBmUDpz2P56E87+SnTs/rr1aNTuWF0SlC/35VVF+ByuQjXRPiev14Rhct1kKraMqprjCgVLS/N6Qqvy2Dg7+PocNkYmqBlR4GFapubu5au5/2Lx/tl314ySY3klnBLbsqrizAoPWdkDleZOef91ewv97SbnTuxH/PPHIbNavXr4zeltcfygZP743A4eO63vcfd7uGpg3jg5P5B/fcttEzAEnSj0UhYWP3Tq95kvbKysl6C3hr5+fm8+uqr3H///a2+7969x3/hB6OcnJxAh9BldKdjaXVXYXZ43sDzDxVSJq9t9T7ckotCxw5sUjVRij5EKI8m+Z35WNaNPScnB50usGU8J3osfzho5LX1nuR8eKyOm/vpyMzM9ENk7UOS3BTZc5Fwo7Y52FvjeV+2uk2+1+yOzE3o5a3vHtSZX5fBxF/H0eI2onTZidGpKKtVsbMon5Oj5UxMatvnf1Psdgm7ZCYnfx/mYiX5NXZu+/UQhbUOAK4dEsM1qaqA/F205lienyijdGgM7+4qa/T2fwyN4fxEWafMZYSGAjoDyt8lKAcOHOCGG27g/PPPZ86cOa2+/6BBg/waT3uyWCzk5OSQmpoa8ASis+uOx7Ko+gDGIs8y20P6DEet1LZpP7a8PKosTuRqCwN7DcRqtXb6Y1l3RCs1NTVg7wv+eF1uzCvnuU2epKNnuJ6vr59KrKFtv+uOUmMtpzjXE+PA5GFEh/QAwC25qd6/D7fkIjJKT5+Ylv9euuPfeHvw93E8bMyiqkRPf60OudwAOHllRzmXTRmFTuW/GnB3YQklNTnoNSpkumRu+261Lzl/4vQhPHDKQL89Vku19Vi+PGgQsbF7Goykq+Qy/n3+pIBObO0I3enLR8B+k1FRURiNxnrXGY1GZDIZUVGtHxnZuXMnN954I9dffz0333xzm2LS6/Vtul8g6XS6Thl3MOpOx9JdY0ehUCCXKQkPjWzzJKTU2KHsOvwHNpcJOyZ0Ok9bws58LLVabb3LgX4ebT2W+cZaLv90PXaXG71awbIbptIrLvh71ptd5eg1YdictcRF9qw3ETnKkECluQizs6JNx6Qzvy6Dib+Oo6OyFoVCgV4TwvPnTuCKRWvIrqzl5bX7eeaskX6I1CPSEEe5OY+y2kou/ewPSkye1p2vzB7DHScHdmCuLcfymXPGoFKp6i1Y5HBLrMou59KRYpXdpmw8+B17CtZy7eT5jd6+p2AtmYXrsNhriAxJYFyfc4kx9OjgKI8K2EyIoUOHUlhYSEVFhe+6jIwM+vXrR0hISKv2lZOTw0033cSDDz7Y5uRcELoTi8NTd6lTG06oQ0BCeG8Ucs/3/MOVWX6JTThx3p7OxTWeetoPL5vMiOTgT87Bs4Lo1EFXcOrAKxp0CYoMSQDAaC7FLXWNCbzdmcnmmbRp0EZyyYhUzhiQCMALv+1md5HRb48Tqo2i2upg2+EyHK5aZDL478UTAp6cnwjvxNHHz0inV6QnZ1q0JTvAUQWvclMBB0q2NXl7Xvketh9ayckDLuaScY/RM2oQv+z+EIcrcOswBCxBHzx4MOnp6bz00kuYTCYOHDjA+++/z2WXXQbAzJkz2bx5c4v29fTTT3PxxRdzwQUXtGfIgtBlWOyedmM69YktxKNUqEkI7wNAYdUBXG7nCccmnBhJkrhhyV9szfcMfjw5YzgXDGv7JOBA0aoaDtR4E3S35KTaUt7RIQl+5u2qEqr1nMV788LxaJWe3ui3+rE3+q5iFzsKKnC63cSFOPj48sncML6/X/YdSPNmDOfJmSO4fJRn1HxFVgElNWIi9LEkyc26A18zOHlyk9tkFW2kX/wYYkNTUCpUDE2eAjLIqwhcSU1Aewm99tprlJSUMGnSJK6++mpmz57N5ZdfDkB2drZvFvKCBQtIT09n5syZAMyaNYv09HQWLFhAYWEha9eu5X//+x/p6en1/m3atClgz00Qgpm3H7BOdeIrZSZHpgHgcjsoNR064f0JJ+a5X3bx+XbP7+HCYSk8enp6gCPyn4g6ixbV2oyBC0Q4YTanBbvLc4bHoPG0QuwbE8pjZ3her2tzSvnfxv0n/Dg/ZR5m1nsb+PHvaL7Ylcyz55zBZaO6VhnIFaM9gyQut8SS7TmBDSYIZRVtRCFT0uc4C+uVmw4TbUj2/SyTyYkKSaK8JnBtXQM6myAhIYGFCxc2eltW1tHT5XPnzmXu3LlN7qfutoIgHJ8kubHaPV1bTnQEHSBSn4BeHYbZXk1h1X60dL7R2q5iWUYuj/+4HYARSZG8f+lJyOWdZ5GTWlsVVZZSwnWx6NVhDcqvVAoN4/ucR6g2qlVtFoXgY3ea0aoMWB2mer3K7zt1MJ9szWZPcRUPfb+V84b0IC60bRNSv87I5bKPV+Nwufm7PIKl150a1P3/22pQfDije0SxJb+CxVuyO3Xpjr9Z7DVsz13JzPSbjrudzWlGfUxJnUapw+oMXLvK4OrGLwhCu3O47ITqolEptOhUJ97OTCaT+UbRjZYiHFLnPsUql8sJCQkhJCQEubzzvEXuLKjk6k/WAhBn0PL19VMJ0agCHFXrFFfnsDPvV9b8/XmTNeaRIQkiOe8CQrXRnDrwck4ffC1huhjf9WqlgrcumgBApcXO/d9tadP+F285yCUf/YnD5SZUo+LHm6Z1yeTcyzuKvimvnKySqgBHEzw2Zf9Av7gx9c6+NS24FrfsPJ8+giD4hVqp5aR+5zNt8NW++vETlRw5gJjQngxJPAUlGr/sM1BGjRpFXl4eeXl5AVukqLVKTVZm/+83au1OVAo5X117CimRrZtsHwyqzMUAGLRRvsnHQtemVKiRy+q3VJzcJ44bxvcDYPGWbFb9XdiqfS5cv49rPl2Lyy0RqVOz4pbTObmPJ0FzS64uucr4pSNTURw5W7ZYTBYFoMC4n5LqQwxPmdbstlpVCDZH/dFy25GzPIEiEnRB6MZOpINLXVpVCGNSzyQuNBWZTLytdCS708XFH/7BoUpP2dJbF43npN5xAY6qbaospQCE62KPu51bcmE0l1BjrTjudkLnNf+cUcQaPF/2b/tqA1ZHy7r2vPrnXm75Yj2S5DmT9Ovc6YxLiaHaUsaav79g5e73fd1jupL4UB2nH+mCs3jrwS75JaS1DpZsw+Iw8eWm+Xy6/mm+2/46AJ+uf5qDpTvqbRttSKbcdNj3s1tyU24qIDa0Z4fGXJf4JBUEQeikJEnizq838efBEgDunjKI68b1C3BUbWN11GJ1eL5kROiP/wVj7d9fsv7AMg6VZXREaIKfSZJEoXE/1ZayJjs/Rek1vHjeGAD2l9Xw3C/H/11LksSzqzK49xtP97fkcD2/3zadYUme+nalQoPJVokkuTF10S92Vx4pc8mpqGVtdmmAowm8sX3O5oLR93PeyLs4b+RdnD74OgDOG3kXKVGDWLrlJYqrcgBIS5zA/pKtlFTn4nTZ2Zn3Kwq5gh6RHb+IlZdI0AWhmyk3Haa0JpdaW/vUKUqShMnWeT8ALRYLd/3vW+7637f1VhUNRgvWZrFw/T4Apqcl8fw5naMkpzFV5qMJRXgzCbq3ZrmytqhdYxLah91pYUfer/y1fymFVQea3O6KUb2Z1t/TWvP5X3eTWex5z3rq5x089fPREVBJknh0+TbfBOneUQb+uG06aXHhvm10KgMKuWdOhre9Y1cza0gPQo6sJLp468EARxN4GqWeEE24759O7SlXCdGEo1SoqbaU4nDbAOgRmcbo1Jn8kbWYT9Y/TYFxP6cPvg6lInDzeESRnyB0MwdKtlFRW0BMaE/GpJ7p132X1+aT51hPaa6K00OvRaXsfPXo9/5vGUsevw0Au+VN3rrtsgBH1Lhf/i7kniOjhQNiw/j0qpNRKjrvmEuVxXMWQCFXYtBEHHfbCH08hVUHqLVXYXNaGixoJAS3uiUmoZqmF9Dy9kYf/uJ32Jxubv1yPaf0jedfK4+Opj9+xjDu+WYTb6zxdHMbGBfGilvOIDlc32BfBk0EVZZSarpgiQtAiEbFBcNS+HjzQb7YfohXZo9Fo1Q0f8duIlQbVW8V0WNXFB2YOIGBiRM6Oqwmdd53c0EQ2sSfPdCPpVHqcUo23JKbAuM+v++/vT318w7e23C09/J7G/bXG6kLFvvLqrnkoz9xuSXCtSqWXX8qEbrO3dmkyuxJ0MN0sc3OY/AuWARgPDKxVOg86o5gh2gjjrtt/9gwHjnSy//PgyX1kvOnV+xk7Ms/+JLzYYmR/DZ3eoPk3MugjTry+J33DF9zrjjS473SYmf53sPNbC0EM5GgC0I34vZzD/RjGTRRqGWe04iHK//2+/7b01M/7+DpFTsbXP/0ip1BlaRXWezM/t/vVFrsyGUyPrt6Sr1T+Z2RJEm+CaIRuuYnuIZqo1AeKVcQZS6dj3dyr04d6vs9Hs8DU4cQrW/8C+j2Ak+yPy4lml/mnnHcnumhR/qtm+3VXXbV49P6J5AY5jkGi7aIMpfOTCTogtCN2BxmJNwAfumB3phQhaeTQLW1jGpLWbs8hr81lZx7BUuS7nK7uWLxGvYeqcV98bzRXaK3syS56R8/lqSI/vVW82uKTCYn/EhfY6NZJOidjbfExbuCaHPm/7KLcrP9uNtM7ZdAlP74JXWGOuU0XXUlWoVczmUjPaPoy/ccpsJsC3BEQluJBF0QuhFveQu0zwg6gEEeh/xIicLhyuBf5be55NwrGJL0R5dv58cjp62vG9eXO08OXIcBf5LLFfSKGcqwnlOJCe3RovtEHknQq47TCUQIPpIk+Upc6q4g2pSW/n0+/+vuZv8+6z5eV27ReeUYT4Jud7n5YsehAEcjtJVI0AWhG7HY2z9BV8hUxBhSAM9CEW53y/oXB0JLP/y9OjJJf/bXPfx3Z4nv5483H+SF33YDcFJqLG9eON5vfew7I28duiS5feUxQvCzOc043Z7R8OZG0P3996lR6lHK1chlSpyu44/Id2bDEiMZmhABwCdi0aJOS3RxEYRuxJugK+RK1Aptuz1OYlg/ys15OFw2SmoO+W3F0mDw3oZ9FNVYGJYYSXpiJMOSIgjT+neC5lM/7+C53/YCEBu7h3PTe3HzF+sA6Bmh58trT+n23RnC9XGE62KJ0Me362tZ8K+6E0RDtU13cGkPMpmMyQPmoFHqu/SXW5lMxpWj+/DQD1tZk11CdnkNvaPbZ0BGaD8iQReEbqRuB5f2/ICK1CehVYVgddSSX5kVtAn6vBnDAVo1Sne4ysJ/19XvUJMaFUJ6YiTDk7xJeyR9ow0o5K0/SXnsqOFzv+3ltbX7sDnd6NUKll0/lfjjTITrbFxuJxsOfEuYLoaU6MG+HufNUcpVTOx3fjtHJ/ibWqmlR+RATLZKQpppp9nav88npg/z3acpWlVIi/bV2V06MpWHl29FkuCTrdk8esawQIcktJJI0AWhG4nQJ+B2u9C084eUTCYjJXooFns1yZFp7fpYJ+rYJEBSqHBFJPgue104LIW0uDB2FhjZWVhJbmWt77acilpyKmr5bne+7zq9WsHQhIh6iXt6YgSRx5nI1tQp/Vq7p8b6g8smMSK5Y0cd21uNtdwzodhaRkJEcH6RE/wnTBfD0B5TWrx9S5P0liTn3UnPyBBO7RvPb/uLWbQlm0dOT+/SZw26IpGgC0I30jNqID2jOmZiYZ/YzvNhefWYPjy3KgOHW8IdlYzp/Ifr3d7Yh7/RYiejsJKdBZXsLKwko8BIRlElZrun5t5sd7Ext5yNueX17tczQs+wpEhficzwpEj6xYTy71UZzSYhuwqNXDislx+ecfCot4KoLjaAkQjBqrkkvTXJuSRJ2JxmTLZKDJrILj2ifsXoPvy2v5i/S6vZnFfO2JSWnZ0SgoNI0AVB6NYkSeKmz9fjcEvIAOmY25v68I/QqTm5Tzwn94n3XedyuzlYbvIl7DsKKsgoNJJdYfJtk2c0k2c088Oeo4uIKGQyXNKxj9yQN0HpSiOF3oWGQjQRqBStW3lWkiQOlWdQWVtEfFhvkiL7t0eIQhBoKklv7ci53WXl98zFAAxNnkKPDhqwCIQLh6Vw+1cbsTpdLN6aLRL0TkYk6IIgtDuny47T7QjK0aqF6/fx635PL+1HTk9HIZf5koDWfvgr5HL6x4bRPzas3kh3tdVORqHRl7jvLKgko6gSk81TutKS5NyrqyXp3g4sbRk9l8lk5JbvwWyvRi5XigQ9yFkdtfxdtBGDNoqkiH6tfj84NklvS1mLRqlDrdBid1l9/di7qjCtmvOG9uDz7Yf4bFs2L5w7GpVCNO/rLESCLgjdRI21gsOVWehUoSRFDkCl6Jil4fccXkN+5d8kRvQhvcepHfKYLZVXWcs/v9sKwJCEcB49Ix1rrYlDOzYCcPeEVL88TphWzaTecUzqfXSVTLdbIqfSxCM/bOu2vYodThtmezXg6crSFpEhCZjt1WJF0U6gxlpOgdEzwTrGkNymL+x1E/K2fkk1aKOoqC2gxtq1E3TwlLl8vv0QpSYbK/8u5KxBzS8EJgQH8VVKELqJaksZOWUZ7C38C0lyd9jjuiU3bslJofFgUPUeliSJm79cT43NgVwm491LTkKjVLBv3z6WzX+QZfMfZN++fc3vqI3kchl9okP57OopPDG95R0WutJkOKPlaJ/3ttafR+g9E3qtDhNWh6mZrYVAqttisbkOLsczb8bwE/obCD2yYJGpCy9W5DUjLYmYEE/p2KLNBwMcjdAaIkEXhG7CO1KpkKtaXet7IrxdXNySk6Kq4Fk046PNB/k5swCAe08ZxLgA1mfOmzG8RUl6V0rOAarMngRdJpMTpo1u0z4ijyToAJW1xX6JS2gf3pISvTochTxwJ/C9CyTZnGYcTlvA4ugIKoWcS0akAvDNrjyqrcEzSCIcn0jQBaGbsNg9o4s6laFD221F6ON8o2X5lZkd9rjHU1Bl5t5vNgMwIDaMJ2cGPultLknvask5eEoeAMK0McjlbVt4KUQT7vvCWWkWZS7BzDuC7h3BDhRDnQWSamxdfxT9yjGe9qVWp4ulO/MCHI3QUiJBF4RuwrdIkbpjV5STyWS+UXSjuZham7FDH/9YkiQx96sNGC12ZDJ495KJ6FTBMR2nqSS9KybnACNSTmdy/zkMSjqpzfuQyWS+UXSjGEEPWpIk+UbQvSPYgWKo8wXB1A3q0Mf2jKZ/jOd9/5OtosylsxAJuiB0ExZ7YBJ0gOSI/sjwjNofrvy7wx+/rs+25fgWFLpj8sB6EzeDwbFJ+sNTB3XJ5Bw8pS0GbSQRbZwg6hUR4knQq61lQTXPQTjK4jDhcnu6FhkCPIKuUmjQKD0TVLtDHbpMJvONov+6v4h8Y20z9xCCgUjQBaEbcEtubA7Pm7JO1fEJukalJzY0BfAk6O4OnKRaV0mNhbu+3gRAn2gDz5w5IiBxNGfejOE8PHUQ/xgawyOnDQ50OEGvbh16taUsgJEITak7Ul23xCRQYkN7EBfWi7BusjjW5aN6AyBJnkEKIfiJBF0QugGroxbpyBI8OrUhIDF4y1xsTjNlNfkBieGOrzdRbvZMCvvvxRMJ0agCEkdLPHLaYG4aFlyj+/5UazNid1r9sq9wXQwjUk5n6sAriTIk+WWfgn95y1tkyAnRhAc4Ghja4xRG9ZpBj6i0QIfSIfpEhzIp1fNlZNEWUebSGQRH4aUgCO3KcqSDCwSmxAUgNqwnEfp44kJ7Ea7r+I4pX+08xJdH+o3fPHEAU/slNHMPoT3tyv+TSnMRSRH9GdZz6gntSy5XkBDex0+RCe0hKaIvenUoNocZuaxtE4KFE3P56D6szSn1LJpWUMmwpMCWGgnHJxJ0QegGQjQRDE0+BYujBr06LCAxyGUKJvSdFZDHLq+1cftXnsWHUiJDeP6cUU1uGxYWxrRp03yXBf9zSy6qjpSi6FSBOaMjdCytykBCuPhdB9LFI3px97JNOFxuFm05yH+SRgc6JOE4RIIuCN2AVhXSbU7lNuaebzZRYvKUU7x90QRCtU2XtgwYMIAvvviio0LrlkzWStySZ8JgW1cQbYwkSZjt1SgVajRKnd/2K3RNBZX7qLFWEBmSQFxYr0CH0+6i9BrOGpTMN7vy+HRrNs+dPRKFXFQ6ByvxmxEEISDM9hokSWr3x/l+Tz6Lt3gWSLpuXF9mDBQ1yoFWZSn1XQ7X+2eSnsvt5LfMRaz+ewmFxvZbAVZovY74O2+LfSWbyS7bQUn1oUCH0mGuHO0pBSuotvDb/hNrSypJEhW1hRQa91NRWxi0v+fOSoygC4LQoWptRl/98cR+57d5ifeWMFrs3PrFegCSwnS8eN6YdnssoeWMR1YQ1aoMaJR6v+xTIVeiVmixOy1U1haTGriFYYVjlNbksvvwnxi0UQzreVrQnN0I1URhsdf4JrB2B2cNSiZCp8ZosbN4y0FOH5DYpv0UV2WTVbTBt0I1gF4dRlrCeOLDe/sr3G5NjKALQjewKXs5W3J+oqgq8LP31UqdbwQ1vyKrXR/r/m83U1BtAeCtOROI0KmbvU9paSkffvghH374IaWlpc1uL7Re1ZEE/UT7nx8r8kg/9EpzkRjNCyI11gpsTgsVtYWoFM3/DXYUbz92k7Wi27xetCoFFw33tLxdmpGL2e5s9T6Kq7LZlruyXnIOYLZXsy13JcVV2X6JtbsTCbogdHFuyU2F6TClNblYHaZAh4NKofGNsBQa9/sWL/G3nzMLeH/jAcDTA/icwT1adL9Dhw5xzz33cM8993DoUPc59d1RnC67b8TS32dPvP3Q7U6Lb2EuIfC8v+8QdXhQdXDxJuhOtwObs/ss3uMtczHZnHyzK69V95UkiayiDcfdJqtoQ7f5wtOeRIIuCF2c1WE62gM9AIsUNabHkZ7oTredkuocv++/2mrn5i/WARBn0PLK7LF+fwyhbarqLCTkzwmicHRFUfCMogvBwbtIUaBXED2WQXN0waQaa/cpc5mUGkevSM9Kqou3tm60u9Jc1GDk/Fhme7X4+/MDkaALQhdXdyQxUD3QjxUVkuT7spBf+bff9//Q99vIM5oBeOPCcUSHaPz+GELbyGUKYkNT0Cj1hPm5H76uTk17Za1IEIKBJLmptRkBMGiCK0Gvu2CSqRsl6HK5jCtGe85irsgqoLjG0uL7elekdrtdOF3242xnPrEgBZGgC0JXZ7EfLWsJlhF0mUxGcuQAAMpN+X4tR/htfxHvrPMk/RcN78WFw7p++7TOJDIkntGpM5k66EqUcv+u5CqTyerVoQuBZ7bX4JZcQPCNoCvkSkLUniTdZKsIcDQd64pRnjIXl1vi8+05Lb6fRhWCJEnU2CqotpZjdTReGqRR+Wfyd3cmEnRB6OIsDk/yq5SrUAbRBC1vgg5w2E+j6LU2Bzd97iltidZreP18UdrS3Xjr0GttRuxOa4CjEeqOTNctKQkWRyeKdp8RdICB8eGM6RkNwKItLS9zidQn4HDZcLkcIEmN1prr1WG+v0Oh7USCLghdnHd0WqcORSaTBTiao3TqUKINyQAcNv7tl0lFj/24nYPlnjMGr54/lrjQ4GjnJnScyJAE9OpwkiMHtNsEZKHlvCPTMpkcvSb4VuZNjhzI4KRJDEw8KdChdLgrRnnKXDbnlZNZXNWi+xQY98GROU0qpbbRlYDTEsYH1WdNZyUSdEHo4nwJepCUt9TVI3Ig0YZk+seP9U1kbau12SW8viYTgHOH9ODSkal+iFDwp8raIrIKN1BUddBX9uBvYboYpqRdQnqPU9GpxdLygWY9Uots0EQglwVfyhEXlkJK9BAiQ+IDHUqHu2RkKgq5J5FevLX5FrzVlnJ2H16NWqkj2pBMbGgK1MnD9eowRqacIfqg+4lYqEgQujjLkdaKwZisJEb0JTGi7wnvx+Jw8o8l65AkiNCpWXChGMEJRqU1uWSX7UAuU3L6kGsDHY7QAYYkT2ZAwjjszpZPRBQ6RnyojjMGJPJTZgGfbM3mqRkjkMsbf990uGxsz12JW3IhlymY0HcWodpoKs1F2BxmNCo9kfoE8b7rRyJBF4Qubnyf87A4alArtIEOpd08+dMO/i71tP566bwxJIW3fYJSbGws11xzje+y4D/eBarCdTFBOZoqtA+VQh1UCxQJR105ug8/ZRaQU1HL2pwSTu7T8EyCJElk5P/ua684OGmSrwNTVEjbViIVmicSdEHo4nRqQ1COnh9LkiTsLkurl37fmFvG//2xF4AZA5O4ZmyfE4qjV69evPzyyye0D6EhSZKoMh9J0P3c//xYTpeDwqoDVNYW0St6CCpC2vXxhM7t76KNlNbkEqaLJb3HKYEOp0PNGtoTg0aJyeZk8ZbsRhP0itoCSqo9i7YlR6bRI2pgR4fZLYkhDEEQAi6/IpM///6MrTkrWnU/m9PFDZ/9hVuSCNWoeOeiCeIUa5Ay26twuj19k/29gmhj9hxeTYHxb8pNh9v9sYTGWewmaqzl7TbfwF8s9hpqrBVUHznD053o1UouSE8B4Isdh7A6Gv6uog3JjEg5nciQRAYnTeroELstkaALQhfmdrs6xZLLTrcDs62aMlMeB0u2U1Fb2KK4/70ygz1Hug/859xR9IwUI6XBymgu8V2OaOcRdKVCRajO00JO9EMPnMOVWazd9xW/7vkoqN+HfK0WbUbckjvA0XS8K0Z7zjoaLXaW7238C21CeB/G9T4HhVwUXnQUcaQFoQs7ULqN7NIdhGqjmNjv/ECH0ySFXEWVpRS328n23FXoNWHo1WGkJYxvsiPAtvwK5v+6C4DT+iVw44T+foklPz+fDz/8EIBrrrmGHj16+GW/3V3VkQRdrdShbaQ1m79F6hOotpRhNBcHdXLYlZlsnt7ienV4UJ/Z8vZnlyQ3Zls1Bm1EYAPqYFP7xZMUpqOg2sKiLQe5YFgKbsmF1VGLXn20NWYw/w67ooCOoB8+fJibbrqJ8ePHM3XqVF544QXc7sa/vdbW1nL//feTlpbGgQMHGtyekZHBGWecwcUXX9zeYQtCp2E5sopfMJ9iLq7KZvfhP30jMzanGUmSMNur2Za7kuKqhotoOFxubljyFy63hF6t4L8X+6+0paioiJdeeomXXnqJoiIx+uov3gmiEbq4Dvmg964o6nDZMNtb1uNZ8C/v4j/BtoLoserG191WFAVQyOVcdqQn+vK9h6kw2/i7aCN/7fuq0fdfoWMENEG/4447iI+PZ9WqVbz//vusWrXKN3JVV3FxMRdccAEKhaLR/Xz77bfccccd9OollvQWhLqCuQc6eCYOZhVtAPBNDpUkNw6XzbdNVtGGBiOgz/+6ix0Fng//584aRe/o4Hx+gofb7aLaWg5AuL5jOuNE6I9OdquyFHfIYwpHuSUXtUe+GBk0wZ2g69WhyGWe/KK7rSjqdeWRMheHy82X29aTU5aB0+0gt2KPOAMVIAFL0DMyMsjMzOT+++8nNDSU1NRUrr32WpYsWdJg28rKSh544AHuuOOORvdls9lYsmQJw4cPb++wBaFTCeYe6OCpD/a27lIpNMiOtN6r2zPZbK+uV0e8q7CSZ1ZmADC5dxxzJ6V1YMRCm8hkjOt9DgMTJxAb2jEDKVpVCDq154ub0VrSzNaCv5lt1UhH6rmDfQRdJpP7vkR4y3K6m2FJkaQnRhCls1Ns9AyaaFUhDOt5mihtCZCA1aDv3r2b5ORkwsPDfdcNGTKE7OxsTCYTBsPRhGLgwIEMHDiQ/Pz8Rvc1Z84cv8RkNpv9sp+OYLFY6v0vtF1XPZZuyY3ZWo2EhFzSdMjru7XHsspUgct1tPxGKVdjd1lwuGz1rq82VaCVheN0ubnu07U4XG60SjmvzxqB1erf35vVaq13OVDvC13tdamRhRGnDwOp495rDapoTBYjlaYioojpMscyUFrzmiyrKfT9DSskbdB/vmoUBlyuYoym0qB8r+wIc9ITyCnbTa3djtnmZHjSZFx2CbM9uH93XVXAEnSj0UhYWFi967zJemVlZb0EvaPs3bu3wx/zROXk5AQ6hC6jqx1Lh2Sh1l4LQPHhMkxFHff6bumxtLiNmB1H3/xdkoRLcuPCjam2xnfaOT+3iHK5lY/2lLH1sGeE6+b0WBwl+ez18+Bo3dhzcnLQ6XT+fYBW6mqvy45U7bJhdpoxYyZMbRPH0k9achwrnNmYXWZkyMnelxf0o7BGZy1mlxmr2caePXs6LN5geU1KksQA/W5q9J5WqFuyQ+klK6OAsgBH1n0FtItLsNU1DRo0KNAhtJjFYiEnJ4fU1NSAJxCdXVc9lpXmQsrzPXXdaSlDCNVGt/tjtvZYSpKEPacQi6PmyM9aamwOlHI1WpUeuUyBThXGyNQJ/F1Ww8JdmQCM6RHFU7MnoWhiWeoTfQ5eqampAXtf6EqvS+/y4B3N6kih0pyCRhZOUX5ZlziWgdSa1+SugmLsJj2h2mgGpwzuoAjbzu5MxemeiE4V2iHJebD9fedX7kVfaiGiVs26XDWZZSE8ctbAoPti1RkHUtsqYAl6VFQURqOx3nVGoxGZTEZUVFRAYtLr2748eKDodLpOGXcw6mrHssLq8E2sjgqPQ6XQdNhjt+ZYDukxiW25K4/8pCBSmXDM7Seh1em445vV2Jxu1Ao57182iVBD+/Q812q19S4H+jXR2V+XDqeNXzM/IVQbRVrCBKINSR322Hr0RIXHYjabKZaVd/pjGSxachwjQ+Nwy+yEaWM6xTHXE5gYg+E1WWszkl2xDYVCQUJYLD/vU+Jw17K73MK4lJiAxtadBSxBHzp0KIWFhVRUVPgS8oyMDPr160dIiFhsRBBOlNNtRy5TIJcpOjQ5b6348N6MTDmDrKINvgmjQL0+6K/+uZd1hzxt+p6YPozBCREBilZorSpLKZLkptpSJhY56Ub6x4+hf/yYQIchtIBeHc6gpInsK97MzCFnovhmFQ63i8VbDnaJBL3CVMCm7B8oM+WjkKs8iy71ORe9un73r22HVrIz71dkx5ztmzP2Qd+E844UsHfLwYMHk56ezksvvcTDDz9McXEx77//Ptdffz0AM2fO5JlnnmHMGPEHLghtkRozjF7R6dhd1uY3DrD48N7EhaVSaS7C5jCjUemJ1Ccgk8k4UFbDo8u3ATCqRxT3Tx3SrrEkJSXx6KOP+i4LJ6bK4pkkIJPJCeuAMqumOCUbLnfwrgcgBAeb04Ldae6QksBgIZPJSIkeQlJEf5QKNbOG9mTJ9hyWbM/hxfPGoFJ03kXnXW4nK3b/j0GJEzl9yHU4XFZ+27uY9fu/5rTBVzfYvk/cSE4eEBzr6QR0OOO1117j8ccfZ9KkSRgMBi699FIuv/xyALKzs30zqRcsWMBbb73lq1mfNWsWMpmMW2+9lblz5zJjxgwKCgpwuVy43W7S09MB+Omnn0hOTg7MkxOEICCTydAoA1/f2BIymYyokEQkSaLQuJ9dlX8SqU/gxs8PYXG4UMplvHvJxHb/sEhKSuK+++5r18foToxHVhAN00Yjl3d8HXqtrYoN2d9Rbi8m2RpHqEH0zBcat7dgLYfKd6NXhzEl7dJAh9PuXG5nvbNaSoUagCtG92bJ9hxKTTZWZBVw9uDOu5qy021nVK/p9IsfjVymQCE30CtmKHsL/gp0aM0KaIKekJDAwoULG70tKyvLd3nu3LnMnTu3yf38/PPPfo9NEITAkMlkHCzdjslWyfqcw/xxwDPq+cjp6QxPCsz8FKFtJEmi6kiCHq6PC0gMOpUBm9Mz2FNlKaEH/QISR3dSXJWN2V5DmC6aaEPnGSTzljGY7dUNkteupqT6ELsPr2ZEyum+VXe9pqclEWvQUGqysWjLwU6doGuUegYkjPP9XGUuZX/xFnrHDmt0+8raIn7YsQCjuZgQTThje59DcuSAjgq3nq776hOEbswtuagyl6JTG9Ao9b4FgILdUz/vAODi9CTKTGVkFecgowdDEyN5eNrQAEcntJbVYfKVWIXrOmYF0WPJ5QpCtTGYamt85TZC+zps3EdJdQ7hulgm9js/0OG0mEFzdADAZKsM2Gu2vZnt1ezM+w2n28723F+YknZJvS8jKoWcS0ak8saaLL7dlU+11U6YVh3AiE+cyVrJ0i0v4pbcDEgYx4iUMxpsE6IJJ1QbzejUGejVYWQVbeCXPR8ya+Tdx10B2eGysb94M/mVf1NRW4DtSOtgjVJPlCGRHpED6Rc/utVzwUSCLghdkNVey4aD3wIwMuUM4sN7Bzii5j318w6eXrETALUsHrlUjVLuJCHUwXuXnIRa2THlEdnZ2fznP/8B4J///Ce9ewf/sQtW3vIWgIgAjaCD58tBIdlUW0txS27kneQLa2dlsnrWKjBoO9cZr7ornpqsXTNBd7mdbDu0EqfbjgwZw3tObfRMwZWj+/DGmiysThdf7czlunGd+8yTQRvJVSc9Q421nL/2L2X130s4Je2yetsMSBhXb7R9SPLJZJfu5EDpNkb1mt7ofv8u2sjWQz/jdrtIjOhLWsJ4tCrPOj5Wh4nK2kK25a5kR94vjOo1o97+myMSdEHogrx9xYGAzD5vrbrJOcAzvxRw+wQ7MmDuSdGM7tlxE7bKy8tZsmQJADfccINI0E9AlcXTeUcpV6NXhzezdfsJ13q+HLjcTmqs5V0y8QoWLrcTs70KAIMmspmtg4tGqUel0OBw2XxfMrqaPQVrqbGWA9A/YRxRTbQ9HdMzmgGxYfxdWs0nW7I7fYIOnvLJMF0Mo3rNYPnOtxjf51xfMt0UgzYSS53uYnWt3fclBcZ9jOw1nf7xY5pc68EtudhfvIUdeb9SWpPLpP4XtSheMYwgCF2Q2d55EvRjk3MAq1NBiUmNSiFnWt/OfWq1O/MsUKQkXB8X0AVPwnVHR+8ra4sCFkd3UGsz+i7XHZHuDGQyme9LRY2tIsDR+F9eRSaHKz3z++LCUukd03gdNniOxZWjPYMTvx0oIt9Y2yEx+luhcT9Lt7yIJLl913nfi+Sy+mPUO3J/odC4v951VeaSJs8E2ZwWZo28h7SE8cddiE0uUzAgYRyzRt6N3dnyrmptStDXrVvHQw89xNVXe1rUuN1uli9f3pZdCYLQDixHEnSlXB3UPdAbS8698qq0OFxutubtwy2J9nid0eCkSZw+5FqG9Zwa0DhUCg0qmWcxGJGgty+T7ejIc2cbQYejXyq62gh6laWUvQVrAc8aE+k9Tm32S/PlozwJuiTBp1tz2jvEdhFt6IHdaWVzzk84XXasDhPbc1cRH5aKWqll6ZaXKK7KAcDmNLPuwDdUmUtxuh3syv+Tams5/eJGN7rv0wZdhVp5dGE7m/PoKtQOl41D5bsxmot916mVWqYOurLFsbc6QV++fDk33ngjRqORbds8vYmLiop44okn+OKLL1q7O0EQ2oG3xCWYR8+Pl5wDHDJ63vgOVVYxf+XajgpL8DO5TB4UrT61Mk+JjdFc7GvZK/ifN7FVylVoVZ1v0UHvaKnVYcLpsgc4Gv9wuGxsP7TKd0ZrZK8zUCmaPzPZOzqUyb09Z58WbTnYKf9u1EotM4b+g7KaPD7d8C+WbX0ZlULLlCP159WWUhxuGwCjUmfSI3IAP+9ayKfrnyK7bAczhv6DEE3z5Xm55bv5ctN8wFPm9cOON1n99xK+3fYa2aVNf84dT6tr0N9++21eeOEFzjzzTIYN85weSUpK4tVXX+WZZ55hzpw5bQpEEAT/8Y6gB2uC3lxyDpBfrUWSZMhkEt9k7MQhhTNvxvAOilDoarTycBwKOxH6OFxuJ0qFKtAhdUl1J4gGsqyprUK1UejUoRg0kThcdl9v8M5MKVeRFNGPA6XbGJI8uVWLMF0+ujdrskvYVWRkZ2Flp2x1GxmSwJnDbm70tmsnz/ddVspVjOtzLuP6nNvqx9iR+ysT+s4GIKcsA7vTxiVjH6XUlMuW7J+abOt4PK1O0HNzc5k+3TObte4f38SJE8nPz291AIIg+J/FbgJApwrOBL0l7C4532XFUlqrosKi4izRZbFTKa3JRSaTE66La9FoXXszyOMZ1OdUQkI636huZ+ItcemM5S0AUSGJDbp7dHYymZz+CWNJiOjT6hVS5wzvxd1fb8LucrNoczbDz+t8CXpHqLaW0SfWM4B0uDKLPrHDUSk1JIb3o/rIpNzWanWJS2RkJOXlDR8sOztbvPEJQhBwu13YnJ4JPTr18WeoB8q8GcN5YnrzIwpZZSFUWNQ8MX24GD3vZP4u2sjm7OXsyv8j0KEAngGlzjii29kMSBhH37hRxIWlBjoU4RitTc4BovQazhrsWWzq023ZuNzuZu7RPcllStySG0lyU1h1gKSI/oCn3AXaVhrU6gT9pJNO4pFHHmHfvn0AGI1G1qxZw913383UqYGdCCQIAjjdDmJDUzBoIltUOxcoLU3Sn5g+TCTnnYzT5aDG6umCEagVRIXASAjvQ//4McSFpQQ6lG7NYjex4cC3fpnsesWoPgAUVlv4bX9xM1t3T3FhvVi3/2vWHViGJEkkhHuOWVbR+gYrtbZUqxP0Bx98EKvVyrnnnovNZmPixIn84x//ICkpiYceeqhNQQiC4D9qpZbRqTOZPGAOsaHB/SHZXJLuTc5tDrMv4WtvvXv35u233+btt98WPdDbqNpa5rscyAWKjuV02Sk0HmBvwdpWtTsTuheL3URB5T72F28JdCht4na72J67kkpzEesPLKvXXaQtzh6cTITOU6a2aMtBf4TY5Yzvcy4Wh4nK2iKmpF2KXK7A6qhlR+4vjE49s037bHUNelhYGIsWLSIzM5ODBw+i1Wrp3bu3+CATBKFNrhrTp9EJo97kfGfebxQY9xEVktimyTutFR0dzcUXX9zuj9OVVdVZQTRMGxPASOqzOGrYcfgXwNN+LS6sV4AjEoJRmSmP3YdXA5ASPaReK73OYG/hOt8iYakxw064i5JGqWDO8F4sXL+PrzNyefOCcYRoxCTrugzaSM4Ycl2967SqEC4e90ibJxq3OkG//fbbeeONNxg4cCADBw5s04MKgiB4vb46EwAZRyv16pa1eDvRVJqLcbmdjS5LLQQX45EEPVQbFVTdUkI0kSjkSlxuJ5XmIpGg+1lG/h9YHbXEhfWiV/SQQIfTZgbN0YmQJlslUcrEAEbTOgWV+8ir2ANATGhP+saN8st+rxzdh4Xr92GyOflmd76vR3p31pozLP3iG++lfjyt/qTbs2cPhYWFJCZ2nhesIHQnRVUHcbochGqjCNcH95LmVRY7/9voWbltzoheDIzz1MzXrTmPCknkACBJbozmYqINyYEIVWgF7+hd3RU8g4FcJidCH0+56bBYsKgdVJgKsDhq0DWzfHqwq7sCqslaQVRI58h3aqzl7Doy8q9VGRje4zS/TYw+KTWW1KgQcipqWbzloEjQgTX7vqz3c91BprrXyeWKjknQb731Vu655x7OOussevbsiUpVf3Rk8uTJrQ5CEAT/ySnLwGguJi4slVG9pgc6nOP638b9mGxOAO6eMojxvRp+oYjQxyOXKXBLLspNBe2eoGdmZvLAAw8A8MILL4gzha1kc5ixOjxtPoPxC6I3Qa+ylIozMn7kdDl8C6SFNrE0emehUqjRqkKwOmqp6SQrijpcdrYdWoVbciKTyT2LESn9t4q0XC7jilF9+PeqDFb+XUhxjYX40MAvQBZIV530L9/lQuN+9hauY3jPaUSGxCNJEhW1BezM+42hPaa0af+tfmd6/PHHAdi+fXuD22QyGXv37m1TIIIg+Id3kSJ9kC5S5OV0uX3lLRN7xTaanAMo5Eoi9PFU1BZQUVvQ7nGZTCbWrl3ruyy0jnf0HIJvBB3wdVSQJDfVlrI2d1gQ6vP2P4f6I9CdlUEThdVRW+95BStJktiV/ztmexUAg5MmEa7z/5fjK0b35t+rMnC5JZZsy+HOKYP8/hidSd0v95tzfmT60BvQq8N81yWE98GgiWLVnveZPeqeVu+/1Qn6L7/80uoHEQShY3h6oJuB4F+k6OtdeRyq9PRrv+uU47/RRxuSqKgtoMpcitPlCKq6ZqE+vTqcvnGjqLGUB2WiFqGLQ4YMCYlKc5FI0P2kyyXo2kjKTHmYrBVIkhR0PfQlyfP6tTlqUSt1KOSeiYjJkQPoEdk+Z/3S4sIZ2zOaTXnlLNpysNsn6HWZrJWoG5mMq1WFtLnVZasT9ORkz+nloqIicnNzkclkpKamEhsbfKcyBaG7sTiOjvjqgnwE/dU/PGfbekWGcP7QnsfdNiokCQAJN5XmImJDj7+9EDgGbQT9tWMCHUaTlAo1odpoqq1lnjp08dHlF94kRKXQoFZ0/tIH75cMh8uG3WlBo9IHOKKjiquyySragNle7btOrw4jJWowaYkT2vXLxBWje7Mpr5wt+RXsLa5iUHzwrrXRkcL1sWw6+AOjUqejUXpeKzanme2HVhGma/0CUdCGBL2iooJ7772XDRs2IEmecniZTMZpp53Giy++iE7X+f8wBaGz8pa3QPCuIgqw4VAp6w55SiFunzwQpeL4SzKE62OPrNTmpMJUIBJ04YREhsRTbS3DaC4OytHRzsiboBs0kV3ieIYe08klWBL04qpstuWubHC92V5NbsUeog3JxIe33wTOS0akct+3W3C5JRZvOcgzZ41st8fqTMb1OZdf93zE30UbUCo8tf9Olw2FXMW0wde0aZ+tTtCfe+45qqureeONN+jXrx9ut5u///6bBQsW8Oqrr4rFigQhgLyTtAC0QVzi8sqfntFzg0bJDeP7Nbu9XKYgPqwXLslFmC54+moLnVNCeD906jBR3uJH3hIXQyefIOoVoo2gZ9QgDJpI9OrgGCWWJImsog2+y2Z7NTqVAblc4dsmq2gDcWGp7fYlKS5Ux/S0JH7ce5hPtmajlMuQyWTdfrXn+LBU5ox9iPzKLGptRlxuJyGaCJIj+6NtY1ejVifoa9as4auvviIpKcl3Xe/evRk4cCA33HCDSNAFIYC8I+gqhQZVGxdHaG+5lbV8tTMXgOvH9SNc17I4h6dMa8+wBD8oqjpIXsVewnWx9IsbXS9xCCaRIfFEhsQHOowuw+my+zr3dIX6cwClXMWQ5JMDHUY9leYiT1mLBLX2KuwOM3anhTBdjG/CotleTaW5qF1bQ145ujc/7j3Mocpa/rUyw3d9d0/SlQo1qTHp/ttfa+9gt9uJi2s4Mz85OZnKyuCf7SwIXZk3QQ/mCaJvrsnE5ZaQyeCOk0ULw66koraQctNhaizl9I8fG+hwhA6iVKiZNugaTLbKoJ/70pnZHJ5J9WZHDXaHpxmAUq5CLlMcs525XeM4b0hP1Ao5dpfbd513NejumqTXWCvYkvMTRnMRTpezwe0Xjf1nq/fZ6gQ9NTWVH3/8kXPPrb/k9vLly+nZU9SFCkIgxYWlolbqfJNUgo3J5mDh+n0AzBrakz7R4sO8K6k6soJouD6u09Qhi65A/qFSaohUipKh9qRRhWB1mLEeGYhRyFUYtA1r/tu7Xv6F33bXS869unOSvubvL7DYa0iK7I9K4Z/+861O0G+55RbuvPNOli1bxoABAwDIyspi/fr1PPvss34JShCEtkmM6EtiRN9Ah9GkDzYeoMrqADwLE7WW0VxCofEANmctI1JO93d4AAwYMIDvv//ed1loGbfbRbW1HKBdejD7W2VtEXsL/qLaWsapA69AqwoJdEhCkDFZKzlYuh2TtZKRvaYHfOJ93VIiuVxBqDYKmaz+BHu9OoxIfft9UXrq5x2+RLwx3TVJLzcd5qKx/2xzvXljWp2gn3HGGXz44YcsWrSIdevWYbPZSE1N5e2332bKlLatliQIQtfncrt57cjCRGN6RjO5d+sXsTGaizhU7ql5tDutqJVav8YIEBYWxkknneT3/XZ11dZyJMkzqhahD74Fio6lVKiptpYBnmQ9mL/YBjuL3YRGpWtQatHZuSU3BUbPGT+TrSKgCXqVuZTtub+gV4dSa6siVBvd6ByPtITx7Xb2qrnk3Ks7Juk6tQGl3H8rt0IbEnSAcePGMW7cON/Pok2VIAjN+X7PYQ6Ue07N3jVlUJveM7z90AEqawvbtZ2Y0Dre8hYIzhVEj2XQRKJSaHC4bBjNxSJBPwHrD3yD3WWhX9xo+sZ1nbZ7IZpw36JWJmslsaEpAYnDYq9hS86PuCUnGlUIg5ImUWjc36APelrC+HZ7T2xpcu7V3ZL09B6nsj1vFaN7zfRbPtzqBL2qqooHH3yQiy66iNNP95xi/vDDD1m7di0vvPACERERfglMEITWMZpLOFSWgU4dSu+Y4aiU/v02f6JePdJaMTlcz5zhvdq0j1BttC+pKjcdFgl6EKmyePra69XhQffaa4xMJiNCH09pTS6V5qJAh9NpOVw2bE7P5EV/1d4GC4VciV4TTq3NSE0bV4P0B7VSR2RIAsXVOQzrMZXEiL70ixt9ZCVRMxqVnkh9ghgoDaDDlVkUVx9if/EWz7wA6v8uzh4+t9X7PP7qII147rnnqKmpoV+/o72LTz31VNxuN/Pnz291AIIg+EeNtZzCqgMcLN0edG/UW/PL+eNAMQC3TUpD1czCRE2RyWS+9mHltQV+i6+ujIwMRo4cyciRI8nIyGj+DgLg+YIInkWlOgtvH/QaSzlOlyPA0XROdZcx7yotFusyaDzPydvnPRAUciUjUk5nbO+zfWd6vO+FiRF9iQpJbPf3/HkzhvPE9GEt3v6J6cO6zeg5gEqhpUdkGj0i04jQxRGui633ry3a1Af9u+++IzLy6B9iamoqL774Iuecc06bghAE4cTV7YGuDLIe6N6FifRqBTdO7H9C+4oKSaK4OodamxGb0+z3jjU2m41Dhw75LgvNc7rsWI6cbo/oBOUtXt7JdBISVZYSog3JAY6o86mbuNZdfbOrMGgjKa7OxmSt7NByXkmSkCS3r85cJpMH/PXpTbibK3Xpbsk5wOQBc/y+z1Yn6FarFY2m4WksuVyOxWLxS1CCILResPZAL6gys2RbDgBXj+lLlP7EToNHGY7WoVeYCkiMaH4lUqF9KRVqpg2+hipLGSGasECH02JhuhhkMjmS5KaytijgCVBnZLJWAKBR6jpFaVNrhR5ZGdUtObHYa9B30Os7s3A91dYyRqVMD6rj2lyS3h2Tc6/iqmz2l2yh5sjfRLgujgEJY9v8vtLq88xjx45l/vz5VFVVHQ2quJinnnqK0aNHtykIQRBOnOVI+61AtwI71oK1WTjdEuCZHHqiDJpI1ApP95ZyU/uUuQitp1SoiTYk+bXNWHtTyJW+08+iDr1tvCUu3lKQrqbu8+qoMpecsp0cKs+gsraQzKL1HfKYrdFUuUt3Ts4Plu7gx4z/Um4qQK8OQ6cOpaQ6hx92LKCo6mCb9tnqEfRHHnmE66+/nokTJ2IwGHC73dTW1tKzZ08+/vjjNgUhCMKJ842gB9FKfma7k3fW/Q3A2YOTGRB74qNPMpmMKEMSRVUHqbaUnfD+hO4tUp9AjbUChVwsVtQWNUeS1q5Yfw6g14T5zrLUWCuIC2vbBPeWKjQeILPQk5Tr1eGkJYxv18drq2NH0rtzcg6QkfcbE/vNbvD72nN4DVsPreCsYbe0ep+tTtB79uzJDz/8wJ9//klubi5yuZzevXszefJkFIqu1QNVEDoLl9uJzelZ3jmYSlw+3nKQCrMdaNvCRE3pEzuC1JhhhOli/LZPoW0kSaLGWo5BG9kp+2D3jRtJ/4SxyGVtm7jcndmdVuxOT2mrQdv16s8B5DIFo3vNRKc2oFO3b3lLRW0hGfm/A6BWaBnT+8x2WevBX+om5N05OQdPk4b+8WMaXJ+WOIHteb+0aZ9t6oNutVp9LRZra2tZt24dOTk59O0r+sgKQiBYHbW+y8Eygu52S7x2ZHLosMRIpvbz3+p2IjEPHlaHib/2L0UuUzAi5QziwgLTK7qtgm1CdWficNkI18VislVi6IITRL1iQnu0+2OYrJVsO7QCt+RCLlMyOvVM9O38hcAfunti7qVRhWCxmwjRhNe73uYwo2zj2blWJ+irVq3iwQcfZMuWLdjtdi6++GIKCgpwOBz85z//4ayzzmpTIIIgtJ23vAWCJ0H/KauAzBJPZ4+2LkwkBD9ve0W35OoUCYXgPyGacCb2Ox9JkgIdSqdmddSyOedHHC4bMmSM7HV6p2pXKkBieF/+zPqU0alnERkSD3hWKN6c8yPxYW1br6PVCfqCBQuYN28eAD/99BMmk4nVq1ezc+dOXnzxRZGgC0IAhOliGNVrJhZHTdAkSa/8sQeA+FAtl41KbZfHcLrsGC0lxBjaf4RLaJx3gSKlXN1g9KizcLrsGM3FVFnK6BM7QnyZbKXucrzsTityuaLNI6JNySxch/XIJP/ByZMDtmKp0HZje5/Nb5mLWL7zLd8SRRKesy/j+pzbpn22OkHPycnx9Tv/448/OPvsszEYDEycOJHc3Nw2BSEIwolRK7VBVVqwu6iKX/Z5umLMnZSGRun/2uSS6kNsO7QCCYkpaZf67YtJeno627dvByA+Pt4v++zKquosUNRZE7Xi6hxf7W9iRN+g+ZIrBAero5a/9i/F7rQwIuV0EsL7+HX/g5MmY3XUEm1IpmeU/+bqCB1Ho9IzM/0mKmuLqbGW4XI7CdPFnFDr1lbPilGr1TidTtxuNxs2bGDSpEmAZ0EPcZpLEASAN9ftB0CjlHPzxAHt8hgGbRQSnvecClOh3/ar0WhISUkhJSWl0TUfhKPckpuqI5102rpaXjDwrigKntPSQsvklu+mrCYfh7NrL+ilUep9K816e1z7k1qpZVzvc+gXJ1pVd2ZlNXko5ApSoofQO3Y4LreLkuq2D1y3OkEfNWoU8+bN48knn0SSJMaNGwfAZ599xoAB7fNBLAhC51FucfL5Ts+b0pWj+xBraJ8uBHp1qK9jTXnt4XZ5DOH4am2VuCUnAOH6zrOC6LF0qlA0Sh0ARnNxgKPpHGxOC3sK1rI5ZznF1dmBDqddyWQyDNoI4Gjf9xNVUVtQb1BTLld02jNQAhwq28XynW/X+4JfbSnlp4z/cqhsV5v22eoE/dFHH6WsrIysrCxefPFFVCoVFRUVvPnmm9x///1tCkIQhBOzdt+XrNv/NYcr/w50KHy1rwKb0w34Z2Gi4/GuKlphKhBn8ALAaC71Xe7MI+gymYwIvWcUXYygt0zdRLWr9kCvy7tgkT8WK8qvyGTH4ZWUOvfillwnvD8h8Hbk/cLJAy6hV8xQ33X94kdz6sDL2Z67qk37bHWCnpyczMKFC1myZAkTJ04EICoqij///FOsJCoIAeByO6mxVlBlKcXpsgc0FqvDxVf7PB9gZwxIZEhCRLs+XnSIJ0G3Oc2Y7dV+2efmzZuJjY0lNjaWzZs3+2WfXZW3/lyrMqBVhQQ4mhPjLXMx2Sq7fMmGP9RNVLvqKqJ1efu8m21VuN1tT6pLa/LYfXg1AFapCkeA37MF/6i2lpNaJzn36hE1sM1lUX5bmUGn0/lrV4IgtIJ39j8EvsXi5zvzqLR5PrzuPqX9Jzt5R9AByk3+K3NxuVy4XGJkqzkhmnDCdXFE6jv/ZNpIfZ06dLMYRW+OdwRdqzJ0i17yoUfOEkhI1NqNbdpHtaWM7bkrkZBQytUkKIf5SquEzk2vCqPMlN/g+qKqA2hU+jbts00LFQmCEDzMQdIDXZIkFqzbB8DA2FBmpCU1c48Tp1WFoFeHY7ZXUVFbQEr04HZ/TOGo3rHD6R07vEuUF4XqolHIlbjcTozm4nZf0r2zMx0ZFewO5S1AvYWYaqyVhGqjW3V/s72GzTk/4nI7kcnkpCdOpfCQf+rZhcAbmDiBVbs/oE/cSEK1UUiSRJWlhOzSHYxOPbNN+xQJuiB0cvUWKVIFLkH/ZV8Ru4s9ZSZzT+rfYROeog1JmCuqfHXoYqJVx+sKx1wukxOhi6O8tkDUoTdDkiRfiUt3KG8Bz2CAUq7C6Xb4vpy0lMNpY0vOj9idFgCG9TiVcHUChYgEvasYnDwZpUJNZuF69hVvQoaccF0M4/qcS//4MW3aZ0AT9MOHD/PUU0+xY8cO9Ho9Z511Fvfddx9yecPKm9raWubNm8d3333H8uXL6du3r+82o9HIk08+ycaNG5HL5Zxyyik8/vjjaLXt0z1CEIKJxeFJ0FUKLUqFfxfQaI1X/twLQIRGwaXDO64ne4yhB7W2KqJCEnFLLhQyMe4gtE1yZBqRIYlEhSQGOpSgZndZcbg8dfrdZQTd08klCqujFrms5es6uNxOth76mVqbEYC0hPEkRvTDbDa3U6RCoAxIGMeAhHF+21+bPsmcTidbtmwhPz+fCy+8EACz2Yxe37o6mzvuuIMhQ4awatUqysvLufnmm4mJieG6666rt11xcTFXX301I0aMaHQ/jz/+OHa7ne+//x6Hw8Fdd93Fiy++yGOPPdaWpycInYp3BD2Q5S17i6v4ca+nBvyCfpHoVP5fmKgp8eG9iQ9v21LKQtvllO3ELUlEhyR1mWXJkyL7BzqETsFcpwa7u4ygA4zrc06rknMAi93kS857RQ8hNWZYO0QmBIMqcykHS7dTazMyecAcwLOgXlvL5Vo9STQvL48zzzyTa665hnnz5gGekfDTTz+d/fv3t3g/GRkZZGZmcv/99xMaGkpqairXXnstS5YsabBtZWUlDzzwAHfccUeD28rKyli1ahX33HMPUVFRxMfHM3fuXL766iscDkdrn54gdDoWu2eSqE5lCFgMr632jJ6rFDIuGtB9PrC7s0Nlu/m7aAN5FXsCHYrQwTRKPX1iRxIX1qtbJeitTc4BDNoIJvSdTa/odAYmTuwS5WBCQwXG/Xyz7RUOle/iYOkOwLOo1U8ZC8ktb9t7ZKtH0J977jmGDx/OkiVLOPXUUwFITExk1qxZPP/88yxcuLBF+9m9ezfJycmEh4f7rhsyZAjZ2dmYTCYMhqPJxsCBAxk4cCD5+Q1nyO7duxeFQkFaWlq9/ZjNZg4ePFjv+uZ0plNOFoul3v9C23X2Y2myGnG5XCjQBOQ1XG628fHmgwBcMDiJGJ2q0x5LAKvVWu9yoN4Xgvl1aXdaMVmNAGgV4UH/3tmWYynmMzTkPX4yl5oeYUMAsNsc2BGDYcenpFfEcCyWo+8twfz3LbTe1kM/Myb1TAYnT+bjvzzVG6HaKCYPmMOO3F/a1MCg1Qn6pk2bWLVqFeHh4b43L7lczm233caUKVNavB+j0UhYWFi967zJemVlZb0Evbn9GAyGem+kdffTGnv37m3V9sEgJycn0CF0GZ31WEZIQ3BhxVIqZ29Zx7+G399disXhaUl4bg8N0PHH0iXZqXIdxuo2Eq3sj0be9rMJdWPPyckJeAvZYHxd1rrKMTs9SXlJfhVVBZ3jvbMlx9LsLsfozMOJhZ6qCSJJb0QwviY7giRJOKRa7FItKpkejbzxssJaVylO7IQrkpvdZ3c9ll2NsbaItPSbj/x09D0jNSadtfu+bNM+W52gy+VyQkIaLkghSVKrW235qzWXv/YzaFD79232F4vFQk5ODqmpqQFPIDo7cSzbzu508/V3ntHzKb1jmT5yYECOpcNlY+2BHciRiI7V0zOy7X/LAwcOZPbs2YBnYligErRgfl1ml2+nplyPQq5kWN/RyGV+W1KjXbTmWJbU5FBdeAA1Cnr1SiJEE9ExQXYCwfya7AiSJLH24BIcLhs9I4fQL7bh+4zRUsyO/C24JReJMfH0ikpvdF/d/Vi2VbAOpKqVOpxuBwp5/bTaYq9G0YbSKGhDgj5gwAA+/fRTrrrqKt91kiSxYMECBg4c2OL9REVFYTQa611nNBqRyWRERUU1fqcm9mMymTyn+BUK334AoqNb16e0tZNcg4FOp+uUcQcjcSxbb+mWgxTVeE7b3jt1iO+DpuOPpZ7wkFhqrOWYHOVd6vcYjK9Lc7ERhUJBpD4eQ0jg5j60VkuOZYKqF5klns8Sq1RFrL79+/l3Jk7Jxr6KtUQY4kiJGoxeE9b8nbqQcH0MleYi7O7aBq8lk9XI3uI/kclBJdOQGNW72ddbMP59C60XF9aLjQe/Y0KfWb7rqiylrNv/NYkR/dq0z1Yn6HfeeSf/+Mc/WLZsGU6nk1tuuYXMzEyMRiP//e9/W7yfoUOHUlhYSEVFhS8hz8jIoF+/fo2O0Ddl0KBBSJJEZmYmQ4YM8e0nLCyM3r1FZweha3M4bchksoCs5CdJEq/84RnN6B8TytmDemC1Bq6eMtqQRI21nMraQtySO+hHdTsrSZKoMpcAdJnuLXVpVSHoVKFYHDVU1hbRM6rznFntCHaplurawxitRSS1MfHozAzaKCrNRb4+8F42h5nNOct97SdHpEwjQh8XiBCFY1SYCtiU/QNlpnwUchUJ4X0Y1+dc9I10PttTsJbMwnVY7DVEhiQwrs+5xBh6NPsYY3ufw8+7FvLJhqeQJInF6+bhdNmJ0Mczuf/FbYq71Z9gY8eOZenSpYwZM4aTTjoJlUrFeeedx48//si4cS3v/zh48GDS09N56aWXMJlMHDhwgPfff5/LLrsMgJkzZ7J58+Zm9xMVFcWMGTN45ZVXqKiooKioiDfffJOLLroIpVL0Qxa6toNl21m15wP+zGrY/ai9/XmwhG2HPQt23HnyIOTywNbqRoV4RjqdbgfVlrI278dut1NQUEBBQQF2u91f4XUZZns1TrfnuITrumYCEhmSAIDRXBzgSIKPQ6r1Xe6O5T+hR/q+Wx0mHC7P34HT5WBLzk9YHZ6OWkOSJouVaIOEy+1kxe7/kRDeh0vHP87sUXdjsdewfv/XDbbNK9/D9kMrOXnAxVwy7jF6Rg3il90f+n7PxxOiCWf2yHuYOvAKRqfOZFSv6Zw+5DrOG3kXBm1Em2JvdYL+5Zdf0rdvXx5++GEWLlzI66+/zr333kt4eDjvvvtuq/b12muvUVJSwqRJk7j66quZPXs2l19+OQDZ2dm+zgALFiwgPT2dmTNnAjBr1izS09NZsGABAE8//TShoaFMmzaN8847j2HDhnHPPfe09qkJQqfj7YGuCsAI+it/eFpHRerUXDO2T4c//rEi6ywuU2EqaPN+du7cydChQxk6dCg7d+70R2hdinf0HLrmCDpApD4e8HwZsTpqm9m6e7G7PZ/LenVYg3rb7sBwZBl3h8tGdukOyk2H2XZoJdVWz6BAn9iR9GxDxw6hfTjddkb1mk56z1NRyJVoVQZ6xQylspEv31lFG+kXP4bY0BSUChVDk6eADPIqWlb37nDbSYkeQnqPU+gXPwan20GVpbTNsbf6r+tf//r/9u47PKoqfeD4d3pJ7wmEEEIJJZRIU5pYKIK9i4p9Lax9VdRdFcW1YeXn6qJYUFfR1bUrRbAhIiBCaAkloYT0nsxk6v39MWRkSCEJSWaSvJ/n2Wcn95577zuHa/LOmXPf8xgXXnhhve1VVVW89NJLXH/99c0+V3x8fKNlGTMzM72vb7nlFm655ZZGzxMSEsJzzz3X7OsK0VX4a5Gi3cWVfLHdU/b0hhP7E2Tw3wqmdXQaPWGmGCqsRZTUHCKFEf4OqUuKDulFetJUKmuLMen8tzhWewo/PIIOnlH0+DD/fwANFHalBg3da4GiI1lslVRYi3C7nWw/tAaX247DZcesDyE5elirl3UX7cOgNfus7llhKWJ3wUb6xNRfMKqkOpc+McO9P6tUaiKDelBSdYCUI7Y3ZH/JNn7K+pDLT5qHy+3kq80vU20rx+12MXHAJQ1e71ianaC/8cYbvPHGG9jtdiZMmFBvf3V1NQkJsjyyEB3Jm6B3cKK08KedKApo1SrmTGj+WgPtLTK4BxXWIspq8nErrlYtLCKaptcaiQtLJi4s2d+htJtgQwRatR6n205ZTb4k6HiePSiz5GN1l2J0Gbvl9JaCimy25v6AorgBcLkd6DRGbE4rNoeFmJAkKcsZoKpry/hk4wLcipsB8WMYkTSlXhub04Je61tRx6A1Ues89joPm/ev4sS+5wKQU5yB3WnjktEPUlS9n43Z37Zvgn7ppZeSnJzMrbfeyqWXXlpvv8lkYurUqS0OQAjROi63E7vLU0HFpO+4ShrlVjtv/rYHgAuH9yYxvPkPdbe3qKCeZBdtRsFNdW0ZoaZof4ckOiGVSkVEUDzFVQe98+27s4KKbDLz11FhKcGmVOO0W8gu3kK4OZa4sO5RjEFRFDLz1wGgUetwumy43E6CDGGEqqLRqDXsKlhPfFiKJOkBKNgYwZXj5lNVW8Ivuz/hp6ylnJx6WQMtW1e2u7K22DvKnluWSUrMcHRaAwlh/aisLWnVOZudoJvNZk499VTmzp3rU2Kxjt1uJyMjg169erUqECFEy9SNnkPHTnF5/ddd1NidANwxKbAqXEQExTMqeQbhQXFo1f6fdtPVuBUXKtTdIgEZ3GMCOq2h299HBRXZbNq/AvAMCtRxuuxs2r+C9KQp3SJJL7PkY7FXAp5vkTRqDVq159kfrcZzj1jslZRZ8okMktkEgUilUhFqiuaE3tP4essrjE05C6Puz8Etoy4Im8N3tNzmtBBujj/6VPWoVVpP9TAgr2IPEw9XbvH8N9O6pL/FD4kuWLCgwe02m40bbrihVUEIIVrO6jgiQe+gKS5Ol5v/+3knAOOTYxidFFgj1Bq1luiQxG6fVLWXA6U7WbVjCRtyvsGtuPwdTrsy6YO7/X105KgxgFupS9BV3gdEM/PXtdligYHMdsTDwkZdEEGGcAy6+vXLj07whH/lle/mk40LvNOSAO8Ag1rlO0YdFdyTkupc789uxU1J9SFiQo498Bwb2pu1u//H2j2foiiKd1pcZv6v3qpQLdXsEfRly5axbNkyHA4Hd999d739ubm56HTd+5eZEB3pyBF0YwdNcfl4y34OlHv+AN1xslQq6G4qLIU4XDYstgqZ398NHDlqDKDXmDConOj1em+S011GjQ265k3layhpF/4TFZyI3VnLhpxvSU86Hafbzh/7VxIXmoxea+STjc8yvt8FxIUlk5pwIj/sfJ8+MSOIDIpna+6PaNQaEiOOvQjn2JSzWLvnU+xOK5NSL0Wt1lDrqGHz/u84bcjVrYq92Ql6SkoKsbGxKIpCYWFhvf1hYWE8/vjjrQpCCNE6Rl0wiuLqsJG+F3/0lJvqExnMOWnHXrzBX9yKiwpLMQadCbO+e6102J7qSoaFmbpmecWj1d1HtY5qEsL7+jucDmc7qsSkSqVCrdKi1xiPatf1R40jzPGY9aE+H1iOZtaHEtGM6RCi4+i1RqalXc+vez7j/XWPodPoiQ/ry7h+FwBQaS3C4fYsLpUYkcrI5On8kPkeVnsN0SGJnD74Gu8UpqO53E7vN0nBxgimDLnGZ79RF8TFYx7wWUjwyGOOpdkJev/+/Zk7dy75+fm88MILzT1MCNFOkqKGkBQ1BPcRX921p7U5Razb76n1e+vEgWjUgblSp1txs3rHuzhcNvrGpNM/fnSLjtfr9d6KVHp9x9eXD1QOl40aWzkA4YfrhHd1u/I3kF28GY1aR1xYn263Oq1BF4RbcVNrr27yOZfuMGqsUqlIjR/rnY/fkNT4sd3i+YzOJiIonjOG3djgvqsnPOnz88CEExmYcGKzzvvV5n9xyqArCDFGNtrmyOS8qraU73e8x1nptzbr/C3+bfPCCy/gdDpZt24dH3/8sXd73aJCQoiO1VFJw/OHFyYKMei4ZkzgjiaqVWpCjFEAlNS0fMGiYcOGsW3bNrZt28awYS0vjdVVHbngRlddoOhodXNHXW4H1bWlfo6m4+nUBmps5dQ6qqmxVTTYpjuNGseF9SE9aUq9b+XM+tBu87Cs+FOfmGF8vulF1u/9kuraskbb1djKWZ/9FZ9vepHkFpRbbPFCRQcOHODaa6/lwIEDaLVaLrjgAnJzc7noootYsmQJ/fr1a+kphRABLqe0mv9lHADgurH9CDUG9shyZFACpTWHqLAU4XQ5Gv2KUjRfhcWToKv48wNQVxdujkNRFJxuO3sL/yApeggR5vhuMUpaVLWfzfu/w6A14XDWNvqeu9uocVxYH2JDkymz5GNzWDDozN3mnhC+hiZOJjq4F+uzv2T7oTWEmWOIDOrhfV7B5qihtCaPCksh4UHxnDroShLCm58jtzhBf+KJJxg+fDhLly5l8uTJACQkJHDOOefw1FNPNboyqBCi7bjcToqq9mPShxBkCG/3Oej/9/NO3IqCWqXi1onHfmDG36KCe7K7cCMKbsot+UQ34yl80bQKi+fZoxBTZLdZ4r2sJo8aWzl2p5Xdjo3kV+7FrA8lNX5slx0tVRSFfSUZ7Mz7FQCDNojkxOGUW/Kpsv45StjV+6EpKpWqyz8UK5onIbwvZ6ffzsGyTHLLsiitPkTp4W9uDdogEsL6MrrPTHpGDGjxuVv8W3b9+vWsXLmSsLCwP0vVqNXMmTOHSZMmtTgAIUTLWe1V/LF/JQAjk6cTE5LUbteqqnWweN1uAM4b2ovkyI5bFKm1wswxh+vSOimpOdSiBL2qqoodOzwPww4aNIiQkK65nH1LKIpCudWToIeZYv0cTceoq/9d93fO4bKD4qla0lXrf7sVF9tzf+ZgWSYAOo2B9KQpRAb3QFEU8kpy2LV3J/0TB5IQlSyjxkIclhiRSmJE266q3eLJq2q1mqCg+uWGFEXpFrVQhQgEHVkD/c3fdlNZ6wACb2GixqhVGu/84dLqls1Dz8zMZPr06UyfPp3MzMz2CK/Tcbrt6A4vyhJu7voJ+pH1v+sWo1EUFy7lz4V6ulr9b7uzlvV7v/Im50GGcE7qdx6RwT0Az6hxuDmOYE0s4eY4Sc6FaGctTtAHDBjA+++/77NNURT+9a9/MXBg4H/1LURX0FGriLrcbl76ybMw0ZikKE5K7jwPB0YFeRKLCmuRZ/RTtJpOY2Bi6iWcNvgq4kK71qhxQ46s/310FYa61TTr6n93FSpU2JxWAKJDenFi33OlRKkQftTiKS633XYb119/PZ9++ilOp5ObbrqJnTt3Ul5ezqJFi9ojRiHEUaz2agD0GmO7zgf+bOtBsks917p90qBONWoWGdwDCjyvy2ryiA3t7d+AugCdxuDvEDrEkfW/NSoteq0J++Hk9cgFmrpS/W+d1sDI5OnklmXSP24Uqm5WUlKIQNPiv+yjR4/mk08+YenSpURGRqLT6Tj77LO57LLLvLWDhRDtq26KS3uOnsOfCxMlhpm5YFjnSnBDTdFEmOMJM8e2ez+JrsVn1UgVBBsisGmNqFUanw+pKpUKt+LqlKuqKopCYWUOsaF/ziUPMoQxIH6MnyMTQkArEnSAvn378sADD7R1LEKIZqqb4tKeieeGAyX8nO15MPDWiQPRaTrXiJpapWZs37P9HUanpyhuDpZlEW6OIcgQ0S0W66m3aqQKDFqTTxuzPpQ9BZvYU/g7g3tM8D7z0Bm43S62HfqZ3LJMUmJGSFIuRBtxu13U2MvbpBRtixP0+++/v8n9TzzxRKuDEUI0jzdBb8cHRF84vDBRkF7L9Sf2b7friMBWbStnW+6PAKQnTSUuLNm/AXWA5qwaGRWcyIFSz38j6/Z+Ts+IVAbEj6mXyAcam9PKH/tWeOfPF1buo2/sCd2mdKYQ7cHpcrB2z//ILtoMwOzxj2NzWvkx830mpV7Wqt8LLf4vcu/evT4/u1wuDhw4gFqtJj09vcUBCCFaxul2YHfVAmDSt0/Jw9wKCx9t3gfA1aP7Em4K7IWJRPupq38O3WcFUfhz1cjM/HV/jqTzZ/3v2NBkQk1RZOX/hsNlI7csk8LKHAbEjSYxMjCf16iqLWFjzjJqHZ7nSmJCkhje61RJzoU4Thtyvqa0+hCTUi/lp6wPvdvdipuNOd8wrt/5LT5ni/+rXLp0ab1tLpeL559/nsTExBYHIIRoGbfbRc+IVKz2KoKNke1yjZd/3onTraBSwW2TOnd1pgOlOyis3EeIMVK+ym+F8sMJulEXhFFXv8RuV3asVSN7RQ4iNjSZrPzfyC3LxOGyse2Qp4744J4TCDMFzgeawsp9bD6wCpfbUzI1OXoYqfFj5GFQIdrAvpKtnDH0RkJN0d4E3aA1MaH/RXy+6aWOSdAbotFomDNnDjNnzuTSSy9ti1MKIRqh1xoZmnhyu52/xuZg0dpdAJw5OJF+0Z271FpR1QGKqvZjtVc1K0EPCgpizJgx3tfdXYW1CCCgks2OdKxVIw1aE0MTTyYxIpVtuT9RbSujwlrE2t3/Y0L/iwg2RnRgtPUpikJ28RayDtd1V6nUDOkxkcTItl1URYjuzOGyEWqKrrfdqAvC6ba16pxt9r2WxWKhrKzs2A2FEAFp3jLP3LnYYCNlVk/d8M6yMFFTooJ6UFiZQ7WtDJvTgkFrbrL9oEGD+PbbbzsousDmcjupri0FIKwbLFB0PCKC4hnX/3z2l2xjV8FGooN7+j05B0/ikFO8BQCdxsgJvad2qgdahegMQoxR5JXvISG8L/DnAmY5xRkEGVr3e6DFCfpzzz1Xb5vVauXnn3+WhYqE6KTmLdvMo8s9f8QjD883T+8Zycl94/wZVpuoWwkRoLQ67/AvUNEcldZilMN/bMJMkqAfi1qlITl6GPFhKYDvHPSiqgOYdMEdnrTrtUZO6D2NHYfWMDzpdMxSclSINjcw4URW73yX/nGjURSFbbk/UVx9kH3FWxmTclarztniBP3LL7+st81oNNKvXz/uuuuuVgUhhGi+A6U7vF+nRQcf/3MfRybnAKWHR88728JEjQk2RKDXGLG7aimtOSQJejMpikJuWSY2pxW1Sk1oG5QN6y6MOt+Ht+3OWrYcWIXT5SA5Zih9Y09Aq9a12/VrHdUYtEHe/37DzbGc2PfcLvHfsxCBKDV+LGqVhh2HfkGlUrPlwGpCTdFMSr2U5OihrTpnixP0VatWtepCQoi2caB0J5XWIuLDUo47QT86OT/SrqKK4zp3oFCpVEQG9yC/Yi8l1YeO2b60tJTvv/8egMmTJxMZ2T4P4gaygopsMvPXUVi5D7vTikatZe2e/5EaP5a4sD7+Dq/TqbAW4nI7UXCTXbSZvPI9DEoYR2xo7zZPmgsqc9hyYBUD4sbQOzrNu12ScyHaV/+4UfSPG9Vm52vVHPSqqipWrVrFgQMHAEhJSeGUU07BZArs+q9CdAVttUhRU8k5wOMrt6JRq3l42vDjuk4giAzyJOgWewW1jup6I5xH2rt3L9dffz0Ay5cv73YJekFFtrf+t15rQqVSoVZpsNgr2bR/BelJUyRJb6GYkCQmDLiI7YfWUFx1gFpHNZv2LycmJIlBPcZh1h//g9iKorC36A92FawHIDP/N+LD+wZ8XXYhuoqymgIqrAU4XY56+/rFjWzx+VqcoG/bto1rr72WqqoqoqOjcbvdlJSUEBMTw/vvv0/Pnj1bHIQQonmcLgeOuhrox7FI0bGS8zp1bTp7kh4V/OfvpZLqQ/SMGODHaAKXoihkHq72AZ75y3qt0adNZv46n+XhRfOY9aGM7D2dwsocduStpdZRTVHVfkqyckmJGUFKzAjUak2rzu1yO9mW+xOHyj3Vl/QaI+m9p0lyLkQH2ZjzLRkHf2hwn4oOStCfeeYZTj31VObOnUtYWBjg+Ur48ccf56mnnuKll15qcRBCiOaxOqq8r1s7gt7c5LxOV0jSzfpQDFozNqeFGlvXmLrTHsos+T6L8jTEYq+kzJLfZOlB0TCVSkVcWB+ighPZU/g7OcUZuBUXe4v+ICG8H0GGsBaf0+awsGn/cm+9+hBjJCf0nnbc37AJIZovK/83Jg64mN5RaWg1bfN8SYsT9C1btvDyyy/71AeOjIzkoYceYubMmW0SlBCiYXXTW+D4RtC7G5VKRXrvqZj0ITKq2ASbowbwjKS7FSeaRh5ktDksHRlWl6PV6EhNGEuPiP5sz/2ZyOAerUrOK63F/L5vuXdl0NjQZIYlntJmCYIQonlUKjV9YoajbsOFv1qcoBsMhga/2tTr9TidzjYJSgjRMJ8EXd/4POqm1I2EN3cU/aGpwzr16HmdcKnjfUyGwyuF1jpqsNorMeiCMOtD6/3ON+iariUvmifEGMmYlLNQcPts35W/Hp3WQFJUGmqVGkVRDq9mWoNBF0SEOR634mJjzjfYnFYAUmJG0D9utEw9EsIPUuPHkJW/joEJJ7XZOVucoA8ePJjnnnuOe++9F73eUy/ZZrOxYMECUlNlZTIh2pP18EiZQWtCo279OmMPTxvOvrJq3l6/t8l2XSU5F80TYY7HoDVTWpMHgMvtQHVUPW+zPpQIsyx001ZUKhUq/px7XmktZk/RJgByy7KIC+3DofJdPlOPzPpQUuPHMqTnJP7Y/x1pPSfSI6J/h8cuhPDoHzeabzL+zdaDPxFkCKv3QXn60L+0+Jwt/gt/7733Mnv2bD799FOSkpIA2L9/PyqVisWLF7c4ACFE87VVBZeNB0r4ZMuBJtt0xeRcURTPiqIOC9Ehx19DvqtRqVToNUZQPIsTmfVhR6+3Q2r8WBmlbUcqlYowUwwV1iJKqnPZV7wVvc6MWR+KGhWoVD4VdU4eeOkxV8cVQrSvHzLfR1HcxIQmtdkaBy1O0FNTU1mxYgWff/45+/fvx2azcfbZZ3PmmWcSHR3dJkEJIRrWK3IQoaboepU1WmJ7fjlnLPqOKpsDnUbNhcOSeH9Tjk+brpicA+w4tIb9pdsxaM1MHni5JJpHKa3Jo8pWeni1S5XPXOa6UVspsdi+QoxRnNj3HPaX7ODXvZ8BYHdYcDhrUas0mPTB6A8/R5GZv46JAy7xZ7hCCDy/Oy8cdV+rp542pFXfkYeGhnLFFVe0WRBCiOaJDkk8rpHf7JIqpv17JSUWG2qVincun8BFw3vTPybUOye9qybnAGHmWCjdjs1pwWKvbPDBvMjISM4//3zv6+5CUdzsOLQGgCBDOBP6X0yNvRybw4JBZybCHC8faDqISqUmxBRJiDESi60Su9OCorhxKW6qbeWEqXVo1FqpqCNEgAg3xbb578cWJ+jFxcW8+uqrZGVlYbfb6+3/4IMP2iQwIUTbyq2wMOXVlRyq9DxUtujiE7loeG/At4RiV03OAaKCe3hfl1bnNpigp6Sk8Prrr3dkWAHhQOkOqmpLAc+KeAadCYNOKt74i81Rg1qlJtgYjsNlxmIrx+V2YtQFo1Fpj2gnFXWE8LdRfWbwy66PSU04kSBDeL1kPcwU0+JztjhBv+eee8jKymLMmDEYja3/ml0I0XGKq2uZ9u+VZJd6HjJ9/pxRXDOmn0+brpyY1zHqgjHrw7DYKyipOUSvqMH+Dikg2J217CrYAHgqi/SKHOTniERdRR0AnUZPmCkWBTeqo8q4SUUdIfxv2VbPoM7+0h0+j+0oeB7juWrCEy0+Z4sT9A0bNvD111/Tq1evFl9MCNF6JdW57C3chEkfQmr8iei0hmYdV2G1M+O179hR4Fmg55Fpw7ltUvdNwCKDErDYKyitPoSiKDJtA8/Dxxq1DofLxqAe4+slgaLjRZjjMetD/6zeogIVvv8uUlFHiMAwLe16/09xiYyMJC4urk2DEEIcW3VtGSU1h6AGBvUY36xjLHYn57yxmo0HPVMX7jx5EH+fMrQ9wwx4UcE9OVi2E7urlmpbGSFG33nmeXl5fPTRRwBcdNFFJCR0/fm9YeYYJg64mMLKfTKfOUCoVCpS48eyaf+KRttIRR0hAkNCeN82P2eLE/TZs2fzwQcfMHv27DYPRgjROKvDU2LRoDU3qwa63eniwrd/4Ke9niXArxvbj2fOGtnt/6BHBv+ZgJZWH6qXoOfm5vLII48AMG7cuG6RoANo1Np2+SMjWi8urA/pSVPIzF/XYB10qagjhP98m7HIW9/8q83/arLtzOG3tPj8zUrQ77//fp+ff/75Zz777DOSkpJQq32/cnv22WdbHIQQ4thaUgPd6XJzxXs/s2znIQAuHtGbVy6U0TbwfMAJNkRQbSujtOYQvaPT/B2S31jt1Rh1QXJfBLC4sD7EhiYfXklUKuoIEShCjX+WFg81Rddb1O14NStBP3jwoM/PycnJgKeiixCiY1jtngc8Tbqm66y63Qo3fvQrH2/ZD8CMQT1ZMmsCGrXMK66TEN4Pq72KmJAkf4fiN06XnV/3fIrZEMbgHuPrfZMgAodKpZKpR0IEmHH9z/e+Ht7rVEJN9dcCcrmdFFcfrLe9OZqVoL/zzjutOrkQou3UTXFpagRdURTu/nwDb63fA8DkvnF8eNUkdBpJzo/UNzbd3yH43Z7CTdicFmxOC1Z7tSToQgjRSp9tepErxz1Wb7vL7WTltje5/KR5LT5nsxL0n3/+uVknU6lUjB/fvIfXhBDN53TZcbhsQNMJ+rxlW3jpp50AjO4VxafXnoJJ16r1yEQXVmOrIKckA4CYkCRiQ7vvNwlCCNFaOcUZ7Cveitvt4ofM9+vtr64tQ63StOrczfrLff311zfrZCqVih07drQqECFE4+pGzwFMuoYT9Oe+385jKzyrgabFh/PVDacRYtQ12FZ0X4qisCPvFxTFU1N7YMJJ/g5JCCE6pTBTLGZ9CKB4nxM7kkFrZnz/C1p17mYl6N99912rTi6EaBt188+h4RH0137dxT1fbASgb1QI3954GlFBzauT3l2VWwrZV7KVSmsx4/tfiLqb1P4uqtpPcdUBAPpED2twNVUhhBDHFhEUx+iUM6mxVzJ54Kw2PXezEvSePXu26UXr5ObmMm/ePDZv3ozZbGbGjBncfffd9SrDACxZsoT33nuPoqIiUlNTefDBB0lL81RfKC4u5sknn2Tt2rXYbDamTJnCww8/LCudii4jMqgHJ/U9D6ujCpPe9yHRDzZlc/N/fwUgMczM8ptOJyFUVhc8FrvTSl75bgAqrcWEm2P9HFH7c7md7Mj7BfCM7KTEyFx8IYQ4Xm2dnEMzE/TTTjvNO4o+YcKEJts2d746wK233sqQIUNYuXIlJSUl3HjjjURHR3PNNdf4tFu1ahULFy7k9ddfJzU1lSVLlnDTTTexfPlyzGYzd999N1qtls8++wy1Ws29997LU089xcMPP9zsWIQIZFqNjjBzDGHE+Gz/avtBrvrPGhQFYoINLLvxdJIjm67yIjwignzrodcl6PHx8dx2223e111JTnGG92vY1IQT0WpkCpQQQgSiZiXoF154off1JZdc0ib1VzMyMti5cydvvvkmISEhhISEcPXVV/P222/XS9CXLl3K+eefz/DhwwHPnPglS5awevVqJk+ezLp163j33XeJjvaUuJk7dy4XXXQR999/P3q9/rhjFSIQfb87n4vf/hGnWyHMqOObG05nYJxMV2gunUZPmCmGCmsRJTWHSGEEAImJid6FiroSRVGosHgWrYowx5MQJosSCSFEoGpWgn7zzTd7X996661tcuFt27bRs2dPwsL+TCiGDBlCdnY21dXVBAcH+7SdMWOG92e1Ws2gQYPIyMhg8uTJAD4fGkJDQ7FYLBw4cIC+fZv/R8hisRzHO+pYVqvV5/9F63WGvlQUxece33CwlHPe/IlapwuzTsNHV4wjNdLo93u4M/TlkYJ10ZRW51NSlUt1TXVAzUNvj75MjZlApKkXZn1Yp/k3agud7b4MVNKPbUf6UhxLi+uvFRYW8swzz/DMM88A8MILL/Duu+/St29fFixYQK9evZp1nvLyckJDQ3221SXrZWVlPgl6eXm5TyJf17asrIygoCBGjx7Nyy+/zDPPPINWq2XhwoVotVrKy8tb9N46YwWanJwcf4fQZQRyX+6zrUGlUhOu6U1hVSQ3rcyh2u5Gp1bx1ISehFuK2LGjyN9hegVyXx7J4q7F4vB8qNm8/TeM6sD7BqI9+rIEK5Df5ucNdJ3lvgx00o9tR/qya/gp60MmDri43na7s5afspZy2uCrWnzOFifojz32mHckb8uWLSxevJiHH36YrVu38vTTT7Nw4cJmn0tRlDZp+/TTT/Poo48yffp0IiIiuO222/jiiy/Qalv29gYNGtSi9v5ktVrJyckhOTkZk8nk73A6tUDvS6fLTv6e3wDQmmK446v9VNrdaNQq3rpkLGcPbp+HuFsj0PvyaE63g6o9e1EUNxFRJpKjBnHgwAFeffVVAG666aZmDzq0tbbqS7fiRlHcaNTdtx5+Z7svA5X0Y9uRvmydQBtItTks1DpryCnewvBep6Lgm6uWWwrJLdvVqnO3+Df2b7/9xvLlywH45ptvOP3007nwwgs544wzmDJlSrPPExkZWW+Eu7y83LOkcaTvinYRERENtu3fvz8ACQkJvPLKK959ZWVlWK1W4uLiWvDOwGzufJUvTCZTp4w7EAVqX1bVWtFoNNQ6XMz9NovCas8H5NcvOYlLRwXmPOJA7cuGRAbHU24poNpRgtlspqqqirfffhuAyy+/3O/v43j7cl/JNrKLNpMaP5b4sJQ2eYaos+pM92Ugk35sO9KXndveos38lv0FKAr/2/hsvf0K0CO8X6vO3eIE3eFweKeb/Prrr8yePRuAoKCgFs1/TUtLIy8vj9LSUm9CnpGRQb9+/QgKCqrXdtu2bZx33nkAuFwutm/f7n149fvvv6dXr17e+eZr1qyhR48eXa4Cg+ierPZq7E43Ww6VkVloAHQsPG8MswM0Oe9sooJ6UG4pwOaoadG3ep2B3VnL7oINOFw29hb9QXxYH6D7JuhCiO6nuraM3/Z+QUFlDiqVmp4RAxiTchYGre83F7sKNrBm18f1Vv48Y9iNxIQ0/E3qoB4nkRI7gqXr5jM17bp6+7VqPZHBCQ0ceWwtTtB79erFzz//jNFoJCsry1t2ccuWLURFRTX7PIMHD2bo0KE8++yz3H///RQUFPDmm29y7bXXAjB9+nTmz5/PqFGjuOyyy7jrrrs488wzSU1NZfHixej1eu8Dot9++y2HDh3iX//6F2VlZbzwwgv1KsEI0VkVV5exJa+MGoeLSpuWx2eM4JYJqf4Oq8voFTWYnpGpmHQhXW50OSv/NxwuGwCDe4xHFUAPwQohREf4bvvbRAX35MLR92F31rJqxztsyP6K8f0vrNc2LjSZM4bd2KLzG7QmzhpxKxFBbTso3OIE/cYbb+TGG2/E7XZz5ZVXEhMTQ0VFBXPmzOGKK65o0bleeukl/vGPfzB+/HiCg4O59NJLmTXLU+w9OzvbOyI/adIk7rrrLu644w5KSkoYOnQoixYt8i5EdN999zF37lwmTpyI2Wzmsssu48orr2zpWxMi4NTYHLzww0YijQ5q7FrunpzG3NOG+jusLsWoCzp2o06o0lrMwbKdACSE92vzPx5CCBHobE4rUcE9GZk8HZ3GgE5joF/sCew49Mtxn/vIB0O3HFzdZNuTUy9r8flbnKDPmDGDkSNHUlNTQ0pKCuApa3jvvfdy1llntehc8fHxvPbaaw3uy8zM9Pl51qxZ3uT9aBEREfz73/9u0bWFCHQ2p4vz3/qBIE05kUboFRHNvVNk5UdxbIqisP3QGgA0ai2p8WP9HJEQQnQ8g9bEhAEX+WyrsVVgNoQ22N5ir2DZ1tcpqTqIXmsmvfcU+sY2/He3btG3o1+3lVY91n/0w5cqlarFybkQ4k/zlm0G4OFpnsW4nC43s979iZVZecwe4SQuxMQpA/p1uSkYgcRqr6a05hBuxeXvUI5bXvluyi0FAPSNPaHLfksghBAtUVx1kB15v3Da4Nn19hl1wYSaojmh9zTCzXHsK9nGT1lLMetDSGjgQc/BPSZ4Xw/pOYlekQPbNFaZkCiEn81btplHl2/h0eVbmLdsM263wnVL1/JpxgEA+kYZGBgbilkf4udIu65ySyE/ZP6HjIPfY1Mq/B3OcXG67GTmrwPArA8jOUqmRAkhREFlDsu3LWZk8nR6hPevt79X5ECmDLmWqOCeaNRaUmKG0zsqjV0FGxs83+qd7+J02QH4fud7bR5v9y2MK0QAqEvO6zy6fAtfbz/IhoOlAJzWP56/TZmFRu2ErlVgJKCEGqNQq7S4FSdWd7m/wzkupTV52J2e1QkH9TgJtVpzjCOEEKJrO1CynR+zljI25Wz6xY1s9nHBhghKqg82uC/UFM0nG58lyBCG2+3kq83/avQ8M4ff0uKYJUEXwk+OTs7r1CXnJ/WO4ZNrJmPUaQBJstqTWq0hIiiekuqDaIOcPP300wAkJSX5ObKWiw3tzbj+55NfkU1MSOeLXwgh2lJh5T5+yvqQyQMvp2fEgEbb7cz7FYPWTJ+YYd5tFdZCgo2RDbY/ZeDlZOb9is1ppbjqIGGmmDaNWxJ0IfygseT8SBNSYgg26DooIhEZlEBJ9UHsVHPVNVej0+j9HVKrhRijCDE2v+ytEEJ0RW7FxZpdHzOyzxkNJufLMl5jQPwY+sQMx+12sm7vZ4QYI4kMSiCnJIODpZnMHNHw6HeoKZrRKWcCYLFX1nsY9XhJgi5EB2tOcg7wzOrtBOmc3DV5ICZdsExVaGdRwT3ZVbAegLKaPGJDe/s5opZxK27UUudcCCG8iir3U2EtZN2eL1i35wuffeePvJvK2hJs3imB43G4bHy/8z0s9ipCjBGcOvhKooMTj3mdhhYpOl6SoAvRgZqbnNf5YddaYoxrGBgXx+SBl7djZCLUFI1WrcPhsnOgdAcutwODLogIc3zAV89xuZ38svsT4kP7kBKbjkYtv9qFECIurA9XT3iy0f0XjZ7rfa1SqRiedBrDk07riNCOSX6LCxHAwoxOQI1RF+zvULo8tUqNXmsiv3wf+/J2YSn/luTkPkSHx5EaP5a4sD7+DrFROcUZ1NjK2VO0iRBTFPFhKf4OSQghxHGQ70OF6EAPTxvOQ1OHHbvhYZP7hpEcGYxJSiy2u4KKbEpr8nArLtyKi7LyMlwuJxZ7JZv2r6CgItvfITbIaq9mT+EmACKCEogLDdwPEkIIIZpHEnQhOlhzk/SHpg5jUKznIVGTThL09qQoCpn569Bp9GhUOsqLa0DxrWuZmb8ORQm8WpeZ+b/iVpwADO4xLuCn4wghhDg2SdCF8IOHpw3n8hMaH+l8aOowHjh9IE63ZxEEGUFvX2WWfCz2SjRqHXpVEJbKWhS3bxuLvZIyS75/AmxESfUh8iv2ApAUOVgqtwghRBchCboQfqAoCjsLG16x8qGpw3h42nCs9irvNllFtH3ZHDUNbncpDqz2au/Iuc1h6ciwmuRW3OzM+wUAncZA/7jRfo5ICCFEW5EEXQg/+HjLfjYeXpBoemoP7/a65Bw8c4vryBSX9mXQBTW43anUYrVXUm4pwGqvRqcxdHBkjTtQsp2qWs89NCB+DDpt4MQmhBDi+EgVFyE6mNPl5qFv/gCgd0QQn1w7mSe/2wrgTc4BrI4/R9CN+oYTSNE2IszxmPWhWOyV3m1qjQpQAQqK4sbmtLD5wCr6xqbTK3KQX0sZKoqbfSXbAAg1RpMYkeq3WIQQQrQ9SdCF6GBLNuwls8iTCD48bTgGrcYnMa+jVesJNcWgKC7UKlmkqD2pVCpS48eyaf8K7za3S0GvCsZk0mO1V2HUBeFw1bIzby3ZRZvpG5tOYsRAvywgpVKpOanvuewq3EBCWD9UskCREEJ0KZKgC9GBah0uHl2+GYDBcWFcMbLxB0UTI1NJjJSR0Y4SF9aH9KQp/LLtG+82lQrCTDGMTp6JVqNjV8EGKqxF2JwWth9aQ1lNvt8WtdBpDQzuMd4v1xZCCNG+JEEXogP9e20WB8o9Dxo+esYINGoZ+QwkcWF9mDzoMnTVcbhVDoannEBSXH9v6cKo4ESKqg6wu2ADlbXFJEUN6dD4FEWRMopCCNENSIIuRAepqnXwz5UZAIxJiuLctF5+jkg0JCIigjOnXtDgPpVKRWxoEjEhvSiz5BMRFO/d53I72ZjzLYkRqSSE922XaSeHyndRUJnDwIQTMetD2/z8QgghAoMk6EJ0kBd+3EFxjQ2A+WekNzkS6nQ5OFSehUkfQrgpTip0BBiVSkVkUILPtoOlOymtOURpzSH2FG2iX+xI4sNS2mzE2+myk5m/DrvTitVexbh+58touhBCdFHy/boQHaC4upZnv98OwGn94zltQEKT7S32CrYfWsPGnG+prC3uiBDFcTLqgzHrwwCosZWz+cB3rNn1XwoqsttkBdLdhb9jd1oBT1lFSc6FEKLrkgRddGuKolBuKaDaVUi5paDdlnJ/atU2qmwOAObPSD9m+yMXKZIa6B1r+/btTJo0iUmTJrF9+/ZmHxcXmsyEARcxNHGyd+XXalsZm/avYO3u/1FYua/V91d1bTn7ij2lOGNDehMTItOjhBCiK5MpLqLbKqjIJjN/HVXWMixOC9UHc9hdEkFq/FjiwhqvrtJSB8treHnNTgDOHdqLMUnRxzxGaqD7j8ViYevWrd7XLaFWqekZMYCE8L4cKtvF7sLfqXVUU1lbzO/7ljFpwKWYDS2bO64oCjvyfkHBjVqlYWCPk1p0vBBCiM5HRtBFt1RQkc2m/St8FqYBsNgr2bR/BQUV2W12rcdWbMHmdKNWqXhs+ohmHVM3gm7UBUsN9E5IrdKQGDmQSamXMLjHBAzaIHqE9/dJzt2Ku1kj6sU1ByipPghAn+hh8nCoEEJ0AzKCLrodRVHIzF/XZJvM/HXEhiYf9zzfrKJK3vxtDwBXjkphcHx4s46z2qsBMOmCj+v6wr/UKg1JUYPpGTEAl9vps293wUbKavLoHz+63gOndVOvKp15FOblgUrBpA+hT+yIDoxeCCGEv0iCLrqdMkt+vZHzo1nslZRZ8uslTi318Ld/4HIr6DVqHp46rNnH1U1xqZvLLDo3jVqLRv3nr1u7s5Z9JVtxuR38tvcLooJ60C9uNBFBcT5TryqdZbgtNjRqHb2jhqBV6/z4LoQQQnQUSdBFt2Nz1ACgKG4U/pxioCgKdmcteq3xcLuWzT8+2qaDpXz4xz4Abhw3gN6RzRsNVxTFO8VFEvSuSa3W0Dc2neyizThcNkpqDlGy9zNMuhCqakvRajyJuFZlQKsz4lTs7C/dTlRwzzZ9PkIIIURgkgRddDsGneehyxp7JQ5nLUZtMAoK1bZS3IqTIGMEBq0Jg858XNd58JtNAATptTxwWlqzj3O67DjdnoovUsGla9KqdaTEjCApcjD7SraSXbQFh8tGXsUe3G4nOq0Rg8Zzn+q1Jkwaz4e7tpp6JYQQIrDJQ6Ki24kwx6NR67A7LCiKG6fbjqK4cSsuAGpqy1GrNESY449xpsb9uKeAZTsPAXDHpEHEhpiafayCQkrMCBLC+hJiimx1DCLwaTV6+saewMkDLyMhvC/K4XvQ4azF7rLWa1839UoIIUTXJiPoottxKy7cbjcAKpUaky4Eq7MWkyGSGnsZiuLG5rRQUn2Q6FbUm1YUhQe/9oyeR5r13D15cIuO12uNDIgf0+Lris5LpzEQF5pMXvlurI5qbA4LRm0QVkdtvbbHO/VKCCFE4JMEXXQ7e4v+QMFFsDECvdaMy+WprqFRa4kJSaLWUYNWo+P3fcsZmTydqOCeLTr/Vzty+SWnCID7Tk0jzKRv8/cg2s/gwYP56aefAOjTp+Pmext0QahUasz6UEy6EO+HyPrtjm/qlRBCiMAnCbroVqpry9lb9AcA8WF9GZU8g/zSfezau5P+iQNJiEqmzJLPhuxvcCtONuYsY3SfGUQENW+6i9ut8I+vPefvEWpizoTUdnonor2YzWaGDBnS4deNMMdj1odisVc2OsfcrA89rqlXQgghOgeZgy66DUVR2Jb7I4riWZFxSM+JqNVqws1xBGtiCTfHoVKpiAxK4ITkqahVmsNJ+jdUWoubdY0P/shhS14ZAH+fOgyTruWfgfcUbmJn3lryK/a2+FjRealUKlLjxzbZJjV+rDwgKoQQ3YAk6KLbyC3L9D5glxIzgiBDWKNto4MTGZE0BZVKTbAxslnlDh0uN498uxmAftEhXDumX6vizK/YQ05xBoWV+1p1vOi84sL6kJ40pd5qoWZ9KOlJU6TEohBCdBMyxUV0G27FhVqlxaQPJqUZKzLGhiYxKvkMwkwxaDXHnke+eN1u9pR46pc/Mm04Ok3LP/9KDXT/27RpExdccAEAH3/8Menp6R16/biwPsSGJpNXkuMz9UpGzoUQovuQBF10G0lRQ4gO6YXTZUet0jTrmKMfEHUrbuxOC0ad76JDFruT+Su2ADC8RwSXjEhuVYxSA93/XC4X5eXl3tf+oFKpDk+9KvVOvRJCCNF9yBQX0a2Y9aGEmqJbdaxbcbHlwCrW7v4Mi73SZ9/LP2eSV+mpW/3YGSNQq1uXUFkdVd7XJn3zVh4VQgghRNciCbro0lxuJ+WWwjY5V0l1LvkVe7E5a1i/9yus9moAyq12nlq1FYDxyTHMGNSysoxHqpveAjLFRQghhOiuJEEXXdqewt/5dc+nbM/92btSaGvFhCQxMOEkwDPSvT77S2odNTz7/TbKrHYAHp+ZflzTEY4cQTfqgo4rXiGEEEJ0TjIHXXRZVbUlZBd55oXX2CtRtcHn0eToobgVF1n5v2GxV/JT1ucs+sWT+E8f2IOJKXHHdf66EXSjLrjZ8+SFEEII0bXICLrokjw1z39Goa7m+YQ2e9AuJWYE/WJHArCzIJczUw9i1LqYf8bxV/uomzYj01uEEEKI7ktG0EWXdKB0B+WWAgD6xY6sV1f6ePWNPYHC6hoOVf5ATJDC3JNrSEs4/oc6U2LTiQ1NRqvRtUGUQgghhOiMJEEXXU6to4as/N8ACDFGkhwztM2voVKpeO03NQdLQxjVo4oRPczYnVZ0zaiX3pRwcyzh5tg2ilIIIYQQnZEk6KLL2Zm3Fqfb89DmkJ4T22Uu9/b8ct7dmINbiWRs73hOHXRGkyuTis5jxIgRZGdnAxAUJA/qCiGE6HiSoIsupahqP/kVewHoFTmYcPPxPbTZmH98+wduRcGg1XDThLMIMvgmcoqiyOIynZRWqyUsTD5sCSGE8B+/PiSam5vLX/7yF8aOHcspp5zCM888g9vtbrDtkiVLmDZtGieccAKXXXYZW7du9e4rLS3lnnvuYdy4cYwePZrZs2ezbdu2jnobIoCoVVpM+hAMWjMD4se0yzV+21/MpxkHAPjrhIEkhvsm59lFm9m0b3mLyzoWVu7j56yP2JjzLQ6nrc3iFUIIIUTn4tcE/dZbbyUuLo6VK1fy5ptvsnLlSt5+++167VatWsXChQt5+umn+eWXXzjllFO46aabsFgsAMybN4+SkhK++uor1qxZw4gRI/jLX/7it2W6hf9EBfdgQv+LGJl8xnHPB2/M37/eBECoUcd9p6b57Muv2Etm/joKq/axef8q3ErDHzgbUmOroNpWRnHVATTykKjfuFwuqqurqa6ult8hQggh/MJvCXpGRgY7d+7kb3/7GyEhISQnJ3P11VezdOnSem2XLl3K+eefz/DhwzEajVx//fUArF69GoBt27Zx+umnExERgV6v55xzzqG4uJiioqIOfU8iMGjUWkJNUe1y7u+y8vhuVz4Ad08eTFSQwWd/bGhvYkKSACiozCbjwGqUZibpdYsUGXRBqFVSAdVfNm3aRFJSEklJSWzatMnf4QghhOiG/DYHfdu2bfTs2dNnrueQIUPIzs6murqa4OBgn7YzZszw/qxWqxk0aBAZGRnMnDmTyZMn89VXX3H66acTHBzMp59+yqBBg4iLa9n847oR+c7AarX6/H93pigKRdX7iAnu3ap5383tS0VReOCrjQBEBxm4YVTvBu+ZAdHjsdlrKbPkcbA0C5fTTWrcuGPGVllTisvlQqc3dap78Uhd4b6sra31ee2vf4uu0JeBQvqybUg/th3pS3EsfkvQy8vLCQ31rU1dl6yXlZX5JOjl5eX1HtoKCwujrKwMgHvvvZcbb7yRiRMnAtCzZ09ee+21FidrO3bsaPH78LecnBx/h+B3Fa6DlDh3Y1SFEacbgkbVuqktx+rL1Qcq2XDQc8/NTg3n4N7djbbVKT1wO4qpVSrYY9lCUVExUZr+Td6TBfZc7IoFda2FHdWd7148Ume+L4+MPScnB5PJ5L9g6Nx9GWikL9uG9GPbkb4UjfFrFRdFUdqk7bx58wD4/vvvCQkJYcmSJVx33XV89dVXLSqTNmjQoGa39Ter1UpOTg7Jycl+TyD8qdZRw2/7NmF2mwkxhDI4aWiLp4c0py9dboUrV6wAoFeYmQfOOgmDtunyjanuVDYfXEFlbRFOytFF1NAvelSDSbqiKBTt+R2t20xSVB/6RHWee/FIXeG+PHJEKzk52W+/F7pCXwYK6cu2If3YdqQvW6czDqS2lt8S9MjISMrLy322lZeXo1KpiIyM9NkeERHRYNv+/ftjsVj4+OOP+c9//kNCQgIAN998M2+99RZr1qxh6tSpzY7JbDa36r34k8lk6pRxt5Wd+34ClRuNRsPw5FMINrV+Nc+m+vKt3/aQWeSZI/7w9OFEhIY065wn9j+L9Xu/orK2mPyqXaTEDSXEGFmvnd1ZCyoFjUZDWHBUp/837cz3pdFo9Hnt7/fRmfsy0Ehftg3px7YjfSka47cn0dLS0sjLy6O0tNS7LSMjg379+tUb9U5LS/Mpm+hyudi+fTvDhw/H7XajKIpPeUZFUXA4HO3/JoRfFVTkUFi5D4DeUUMJM8W0y3VsThfzlm8GYGBsKFeOTGn2sTqNgVF9ZhBmiuGE3lMbTM4BrPYq72uzrnnJvxBCCCG6Jr8l6IMHD2bo0KE8++yzVFdXs2fPHt58800uu+wyAKZPn86GDRsAuOyyy/j000/5448/sFqtvPLKK+j1eiZPnkxwcDBjxozhlVdeobi4mNraWv7973+j0+kYPXq0v96eaGdOl53th9YAYNQF0z9uVLtda9HaLPaX1QDw6Bkj0Gpa9p+NXmvkxL7nequ7NKSugguASS8JuhBCCNEWqmvLWLV9Ce//+igfrJvPT1kfYnM2/HBudtFmPvv9Bd5d+xBfbFpIbllWB0f7J7/WcnvppZcoLCxk/PjxzJ49m3PPPZdZs2YBkJ2d7a2eMGnSJO666y7uuOMOxowZwy+//MKiRYu8X0U///zzREREcO655zJx4kTWrFnDa6+9RkREhN/em2hfuwrWY3N6kubBPcajbae64dU2B4+vzABgVK8ozh/aeJLdlKPnnRdXHWRfyZ/fCsWFJnNy6izGpJyFQSfLywshhBBt4bvtb6PXmrhw9H2cNeJWyi2FbMj+ql67kupD/JT1ESOTp3Pp2H8wuOcEVu94hxpbhR+i9vNDovHx8bz22msN7svMzPT5edasWd7k/WjR0dEsWLCgzeMTgancUuhNbuNCk4kN7d1u13rxxx0UVXtW9Zx/xohWlXE8WnHVAX4/vNKoWqUmMWIgZZYCbI4aDLogVBz/NUTraTQabxUpjabpB4GFEEIELpvTSlRwT0YmT0enMaDTGOgXewI7Dv1Sr+2ugvUkRqaSGDkQgL6x6ezI+4U9hZsY1mtyB0fu5wRdiNbQa41EBydSbilgUI/x7XadkhobC77fDsAp/eI4fUBCm5zXrA9DpzFgc1rYtG8523J/Omp/KKnxY4kL69Mm1xMtk56ezv79+/0dhhBCiONk0JqYMOAin201tgrMhtB6bUuqc0mMSPXZFhXUk5LqA+0aY2NkuULR6Zj1oYxMPoOT+p2PsR2ngzy9aiuVtZ6HjefPSG+T0XMAsyGUMSln4lbcVNeWUVp9iFpHDRyuJGqxV7Jp/woKKrLb5HpCCCGE8Ewv3ZH3C8N6nVJvn81hQa/1rahj0JmodfhnsTpJ0EWnpFKpCDKEHbthK+VWWPi/nz3TrM4eksiJvdu2QoxZH4ZBa0J1uGa7xVZBac3hRP2wzPx1LVorQAghhBANK6jMYfm2xYxMnk6P8P6NtAqcv7mSoItOQVEU9pdsw+myd8j15q/YQq3ThUoFj50xos3PX2bJx+GyEWKM8tl+5Pxzi72SMkt+m19bNM1isbBjxw527NjhfVBdCCFE53WgZDsrt73JmD5nMriRqbFGXRA2p+/vfJvDglHX+vVVjock6KJTyK/Yy/ZDa/gp60Oqakva9Vq7iyt5Y91uAC4/IYW0hLavBmQ7PFKu1egINUV7RtJVKrQa/VHtJEHsaNu3b2f8+PGMHz+e7du3+zscIYQQx6Gwch8/ZX3I5IGX0y9uZKPtooJ7UlKd67OtuPogMSG92jvEBkmCLgKew2VjZ57niWu1WoNZ335TWwAe/nYzTreCTqPmkWnD2uUaR5ZS1Gr0hJtjCTfFolFrj2onK8wJIYQQreFWXKzZ9TEj+5xBz4gB9fYvy3iN7CLPQoQD4sdwqHwXB0p34nQ72JW/nkprMSmx6R0dNiBVXEQnkJX/m3dRgcE9JtRLYtvS5kOlfLApB4AbTuxPn6j2WTQowhyPWR+KxV4JgEql5uhnUM36UCLM8e1yfSGEEKKrK6rcT4W1kHV7vmDdni989p0/8m4qa0u8+UVEUDyTBlzK+r1fUm0rI9wcy2mDr8Lsp8UDJUEXAa2spoADpTsASAjr2+5fNf396z8AMOs1PHj60Ha7jkqlIjV+LJv2r2i0TWr82DarHCOEEEJ0N3Fhfbh6wpON7r9o9Fyfn3tHp9E7Oq29w2oWSdBFwHIrLrbl/giAVq1nYI+T2uU6/1y1naKiIi42x/D1Ds/8s9smDiI+1NQu16sTF9aH9KQpZOav846kg9RBF0IIIbo7SdBFwMou2kK1rQyA1ISxGLRtPx973rLNPLHaM0L/7YHfAIgw6bnnlCFtfq2GxIX1ITY0mTJLPjaHBYPOTIQ5XkbOhRBCiG5MEnQRkGpsFewp/B2AcHMciRED2/wa85Zt5tHlW7w/H6zwzEO799QhhJv0jR3W5lQqFZFBbbNKqRBCCCE6P6niIgKSQWemd1QaapWWtJ6T2nxE+ejk/Eh1q4cKIYQQQviDjKCLgKRV60hNGEtyzDAM2radC95Ucg7wxHdb0WnUPDxteJteV3QOJpOJgQMHel8LIYQQHU0SdBHQOjo5r1PXRpL07mfIkCH88ssv/g5DCCFENyZTXERAySneQnVtWbucu7nJeZ1Hl29h3rLN7RKLEEIIIURjJEEXAaOk+hA7835lze6PKajIafPz25yuNj+nEEIIIURbkykuIiC43S625/4EeOafhwfFtdm5t+WX8681mby7cW+Ljnto6jCZ4tINVVRU8NtvnpKbY8aMISwszM8RCSGE6G4kQRcBYU/RJmrsFQCkJpx43HPPnS43n207wCtrMlm9u8BnX0KoibxKa5PHS3Lefe3atYtLLrkEgOXLlzNq1Cg/RySEEKK7kQRd+I2iKJRZ8imvKSAzfx0alZao4J70DB/Q6nMWVll5fd1u/v1LFgcrLN7teo2ai0ckM2dCKmOSopucjy7JuRBCCCH8SRJ04RcFFdmeJe5tlVTWFuN02dGodQzqMb7FNc8VRWHd/mJe/jmT/27eh93l9u7rFW7mpnEDuHZMP2JD/hyVr0vAj07SJTkXQgghhL9Jgi46XEFFNpv2rwDA5rTgdNkB0GuN7Mz7BZMuiLiwPsc8j9XhZOmmffxrzU42Hiz12Xdqv3humZDKWYMT0Woafhb66CT9/lMGSXIuhBBCCL+TBF10KEVRyMxfd/i1G4u9EgC1WotRFwxAZv46YkOTGx1Jzymt5tVfsnhj3W5KLDbv9mCDltmj+nLzuAEMjg9vVjwPTxuOw+GgqKiIB04dfBzvTAghhBCibUiCLjpUmSXfm5SrUFNl06NRWYkNDfcm5BZ7JWWWfCKDErzHud0K3+3K4+U1mXy5/SCK8uc5U2NCmTMhlStHpRBq1Lc4pgdOHcyOHTuO740JIYQQQrQRSdBFh7I5aryvc8qqySm1o0KDQ7GTHKk/op3nAc8Kq50lG/bwyposMosqvfvVKhVnDUlkzvhUTu0f3+J560IIIYQQgUoSdNFhFEWhsHIfNoeFvCo3OaXVnu3gfZ0c6ZnmcrDCxfzv1vHOhr3U2J3ec0SZDVx/Yj9uPGkAvQ+3FUIIIYToSiRBFx3C5XaScfB78sr3UFRdzqEKOHoh25zSamrsTiprdcz//lfgz1HxUb2imDMhlYuHJ2PUaTo0dtG9hIWFMWXKFO9rIYQQoqNJgi7aXa2jht/3LafSWsS+shp2Fmgx690Nti2stvHZjlBAVa92uRAdoX///ixdutTfYQghhOjGJEEX7arCUsTv+5Zhc1rIKa1meZbCV1m96BNh5eTkMiJMDm/bMquOH3Ii2FUSxKn94njviok+tcuFEEIIIboDSdBFu8kr30PGwe9xKy5ySqv5zx9a1uwPB1TsKgliV4mZxFAbQXoXNXYNBysN1E1rWbW7gFd+yZK65EIIIYTodiRBF+1iT+EmdhWsB0Ct0lBhH8Ka/YVHtVJxsNLY8cEJ0YTCwkK+/vprAGbMmEFsbKyfIxJCCNHdSIIu2oVJHwKAQWsmvfdUpqbFUuve7F2181gemjpMRs+FX+zfv5+77roLgLS0NEnQhRBCdDhJ0EW76BHeD5fbQUxIEkZdEOBZtXNPcRXv/Z7d5LGSnAshhBCiO5MEXbSJckshTred6OBE77ZekYN82mQWVvDl9oNNnkeScyGEEEJ0d+pjNxGiaYfKd/Pb3i/4Y99KqmvLGmxTarFxzuLVVNQ60KhVXDGyT702kpwLIYQQQkiCLo6Doijsyl/PlgOrcCsu3Iobi72yXjuHy82lS35kV3EVAC+eO5q3Z03goanDvG0kORdCCCGE8JApLqJVnG4HGQe+p6DSM5/coA3ihOSphJli6rW989P1fLcrH4Cbxw3g5vGpAD4JuSTnQgghhBAekqCLFrPaq9m0bzmVtcUAhJliSO891fsw6JFeWZPJK79kAXBa/3ieP3e0z35JzIUQQgghfEmCLlqk3FLI7/uWYXdaAUgI60ta4slo1PVvpZVZedz+qacWev/oEJbOnoROI7OqhBBCCCGaIgm6aJFyS743Oe8fN4qUmHRUKlW9dllFlVyy5EdcboVwk57PrjuFCLOho8MVosViYmK45pprvK+FEEKIjiYJumiR3lFDsdiriAxKID4spcE2ZYcrtpRb7WjUKj64ciKpsWEdHKkQrdO7d2+effZZf4chhBCiG5MEXTTJ6XJQ66gm2BgBgEqlYnCP8U20d3PJkh/JKvJUc3n+nFFMSe3RIbEKIYQQQnQFMiFYNMpqr2Ld3s9Yn/0VtY6aZh1z12cbvBVbbjxpALccrtgihBBCCCGaR0bQRYPKagrYtG8ZdlctAAdLd9IvbmSTx7z6SxYvr8kE4NR+8bx43ugG56cLEcgOHjzIW2+9BcDVV19NYmJi0wcIIYQQbUwSdFFPblkWW3N/RFHcAPSPG01KzIgmj1m1K4/b/vcbAP2iQ1h6lVRsEZ1Tfn4+zz33HADTp0+XBF0IIUSH82uCnpuby7x589i8eTNms5kZM2Zw9913o1bXT+yWLFnCe++9R1FREampqTz44IOkpaUBMHTo0Hrt7XY777zzDmPGjGn399FVKIqbrPz1ZBdvBkCj1jIs8VTiwpKbPG5XUSUXv+2p2BJm1PHZtacQKRVbhBBCCCFaxa8J+q233sqQIUNYuXIlJSUl3HjjjURHR3tLnNVZtWoVCxcu5PXXXyc1NZUlS5Zw0003sXz5csxmMxkZGT7tN27cyD333MOwYcMQDVMUhTJLPjZHDQZdECGGSDIOfk9h1T4AjLpgTug9jVBTVJPnKbfaOWfxasqsdtQqFR/MnsTAOKnYIoQQQgjRWn5L0DMyMti5cydvvvkmISEhhISEcPXVV/P222/XS9CXLl3K+eefz/DhnlUnr7/+epYsWcLq1auZOXOmT1uXy8Wjjz7KPffcg9Fo7LD305kUVGSTmb8Oi73Su83tduF029FrTYSb40jvPQWD1tzkeZwuN5cu+ZHMwxVbnjtnJFOlYosQQgghxHHx2yThbdu20bNnT8LC/hxtHTJkCNnZ2VRXV9drO3jwYO/ParWaQYMG1Rs5B/j000/R6/WcccYZ7Rd8J1ZQkc2m/St8knMAtUqNzWkl1BjN6D4zj5mcA9z9+QZWZOUBcMOJ/fnrhIHtErMQQgghRHfitxH08vJyQkNDfbbVJetlZWUEBwf7tD0yka9rW1ZW5rPN7XazaNEi7rnnnlbFZLFYWnWcP1itVp//bw5FUdh2cA0ul8vzMwoq/qyyYtaFUWu3UGu1oVLZmzzXG+v38n8/eyq2TOwTw1PT01oUSyBpTV+KhnWFvqytrfV57a/fC12hLwOF9GXbkH5sO9KX4lj8OgddUZQ2bfvDDz/gcDg47bTTWhXPjh07WnWcP+Xk5DS7rdVdTomjAACnUotLsaNXh/gk6RYsbNr+KyZ1eKPn2VBQw12rPHPVE4N1/OOECHZnZbYq/kDSkr4UTevMfXlk7Dk5OZhMJv8FQ+fuy0Ajfdk2pB/bjvSlaIzfEvTIyEjKy8t9tpWXl6NSqYiMjPTZHhER0WDb/v37+2z79ttvOeWUU1pde3vQoEGtOs4frFYrOTk5JCcnNzuBKKjKpiLPTK2jBrfTjgpQaVyY9b7fZCQmxBMb0qfBc+wpqebB/63CpUCoQcv/rpnMwNjQBtt2Fq3pS9GwrtCXR45oJScn++33Qlfoy0Ahfdk2pB/bjvRl63TGgdTW8luCnpaWRl5eHqWlpd6EPCMjg379+hEUFFSv7bZt2zjvvPMAz4Og27dv58ILL/S2URSF1atX8+STT7Y6JrP52POuA43JZGp23GFKJHaXFZuzGhUq1GotQYZQ1GqNT7vQ4MgGz1lutXPpf9ZSZnWgVql4f/YkTkiOb5P3EQha0peiaZ25L/v27cs//vEP72t/v4/O3JeBRvqybUg/th3pS9EYvyXogwcPZujQoTz77LPcf//9FBQU8Oabb3LttdcCngVC5s+fz6hRo7jsssu46667OPPMM0lNTWXx4sXo9XomT57sPd/BgwepqKiQRUWaUFqdh83pmU+rVmsJNUbVS87N+lAizPWTbqfLzWXv/MTOQs/Dpc+ePZLpA3u2f9BCdLCEhATuvPNOf4chhBCiDeSWZfFT1ofEh6UweeCsRtv9lPUhewv/QKX6s36KRq3l8pMe6YAo6/PrHPSXXnqJf/zjH4wfP57g4GAuvfRSZs3ydF52drb34axJkyZx1113cccdd1BSUsLQoUNZtGiRTxnF4uJiAKKjozv+jXQCewv/YHfhBsz6ECz2qgaTc4DU+LENThG654uNLM88BMD1J/bj1olSsUUIIYQQgSvj4A/sKlh/zDVd6gzrdQrpvae0c1TN49cEPT4+ntdee63BfZmZvg8dzpo1y5u8NyQ9Pb3eMcJjb9Fmsgp+AyDcHMeIpCnkFG/xKbVo1oeSGj+WuLD6c89f+3UXL/20E4CT+8ax8LwxrZ7nL4QQQgjRETRqLWcO/yvr9n6Oy+30dzgt4tcEXbQ/t9tFfsUeAEz6EEb3OROTPphekYMOryRqwaAzE2GObzDp/n53Pn/9eB0AKVHBfHTVyei19Ufehegq9u7dy1NPPQXAfffdR0pKip8jEkII0RqDe4xvUfv8ij18vmk7lbUlhJtiObHfuUQH+2fqtCToXZxarWF08ky2HfqJ1PgTMek99eVVKhWRQQlNHrunuIqL3v4Bp1sh1Kjjs2tPISrI0BFhC+E3paWlfPTRRwDccMMNkqALIUQ3EGqMQq1Sk540Fa1Gz+b9K1m+dTHnj/wbRl3QsU/QxiRB76IURfGOiOu0BkYknd6i4yusds55YzWlFjtqlYr/XDGRwfHh7RCpEEIIIYR/DU/yXUNnZJ8Z7C3ezP6S7QyIH93h8aiP3UR0NjnFW8g4+D2K4m7V8S63m1nv/sSOggoAnjnrBM4YJBVbhBBCCNE9qFVqgvThWI94Xq9Dr++Xq4p2k1Ocwc68XzlUvovM/N9adY57v/idb3d6KrZcO6Yft0/qPAs4CSGEEEK0hKIo/Lb3S0pr8rzbXG4nVbUlBBsjmziy/cgUly5kX/FWduatBcCoCyYpanCLz7F43S5e+NGzUteklFhevkAqtgjRnSiKgsPhwOVy+TuUNmOz2bz/L7/PWk/6se1IX/rSaDTodLoO7YsaWwXLtr7OlCHXEGKMpLq2lF/3fMrJqbPQa41s2rcctUpDUtSQDovpSJKgdxH7ireyI+8XwJOcj0k5E7M+tMlj5i3bDMDD04YD8OOeAuZ87Bl17xMpFVuE6G6cTidlZWWYTCY0mq7z375er6dPnz7o9Xp/h9KpST+2HelLX3a7nYqKCiIiItBq2y41XbLm7wAoisvn59nj5+NWXFRai7zlF8f3v5D12V/xxR8LcbhsxIT0YtrQG9Bp/PNvJAl6F7CvZNsRyXlQs5PzR5dv8f585agULnzrBxwuNyEGHZ9ddwrRwcYmziCE6EoURaGsrIzo6OguN6JX922A0WjsUh88Opr0Y9uRvqwvKCiI4uLiNv0dNHv8/Eb3hRgjuXrCk96fDTozEwZc1CbXbQuSoHdyueWZ7ClZDxxOzvuc1eLk/NHlW/jXmkxKLDZUKnjvigkMkYotQnQrDocDk8nU5ZJzIUTnoFKpMJlMOBwO+WYBSdA7NbfiJKfEM1/coA1idJ8zMRtalpzXKa7xzId7+syRzBzsn6L8QgSCPn368O9//9v7urtwuVwykieE8CuNRtOlnn85HpKgd2JqlZYRiVPZVfwrw5NOI8gQ1mT7xpLzI1XW2tsyRCE6naioKC66KHC+5hRCCNH9SILeyQUZwjmp33nH/Fq6Ock5wGMrMlCpVN4HR4UQQgghRMeSBL2TOVi6E5fbSYz5z+XH2yo5r1PXVpJ0IYQQQoiOJwsVdSIHSzPZmvsjO/J+4VBFlr/DEaJL2rlzJzNnzmTmzJns3LnT3+EIIYTohiRB7yQ8yfkPABi0JsJMcc0+9pIRyaT3jGh2+4emDpPRc9FtVVdXs3btWtauXUt1dbW/wxFCCNENSYLeCeSWZXmTc73WxOg+ZxKkb/qBUEVR+HFPAecsXs2Qpz9nU25Zs64lybkQQnRtNTU1DBw4kLfeesvfoQghGiFz0ANcblkWGQe/B0CvMTKmz5kEGyOwWCwNtne63Pxv6wGeXb2N9QdKvNv1GjWXj+yDUavhlV8anh4jybkQQnR9u3btQlEU+vfv36HX3bp1K88//zybNm1CURTS09O55557GDRoUIfG0ZXY7XZefPFFPvvsMyorK0lNTeWOO+5g/Pjxxzy2pqaGxYsXs3nzZjIyMqioqOCJJ57g/PPPb3XbuXPn8r///a/Ra/7444/ExTV/BkB3Jgl6ADtUtss3OU85i2Bjw1NVamwO3vxtDy/8uIPs0j+/lo8w6blp3ADmTEglIdQMQEywsd5Do5KcCyG6u1NPPZXhw4fz/PPP+7zuarKyPIM0AwYM6LBrbtu2jVmzZpGQkMBf//pX3G43//nPf7jiiiv46KOPSElJOfZJRD1z585l2bJlzJ49m+TkZP73v//xl7/8hbfffptRo0Y1eWxZWRkvv/wyPXr0IDU1ld9+++24215yySWcdNJJPtsUReGRRx6hZ8+ekpy3gCToAarWUcPW3B8BT3I+OuXMBpPzgqpa3vghk1fWZFFm/bOGeXJkEHdOGszVY/oSbND5HFOXiNcl6ZKcCyFE5/fxxx/z6aef8s477zTZLisri4iICGJiYjooMnjxxRcxGo188MEHRER4/padffbZTJs2jeeff56FCxd2WCxtyWazYTAY/HLtLVu28NVXX3Hvvfdy3XXXAXDuuedy5plnsmDBAj744IMmj4+NjeXnn38mJiaGjIwMLrzwwuNum56eTnp6us+2DRs2YLVaOeuss1r4Drs3mYMeoIy6IIb3OhWD1szolDMJMUb67N9ZWMnj6w4x+Nlv+OfKrd7kfFSvKN6/ciKZc8/lrxMH1kvO6zw8bTgPTR0mybkQQnQRmzZtala7zMzMetNbPvzwQ9LS0nj88cfbZSXHDRs2cNJJJ3mTc/AkfWPGjGH16tXU1NS0+JyHDh3iwQcf5LTTTmPo0KGMHz+eG264gX379vm027p1K7fddhsnnXQSQ4cOZfr06fzf//2fT5sffviBK6+8khNOOIFRo0Zx9913U1pa6tPmmmuu4dJLL2XDhg1cccUVDBs2jMcff9y7f926ddxwww2MGjWKMWPGcOONN7J///4Wv6/m+vbbb9FoNFxyySXebQaDgQsvvJBNmzaRl5fX5PF6vb7ZH9Ja0vZoX375JSqVijPPPLNVx3dXMoIewOLC+hAd0guN2vPPpCgKP+4t5Nnvt/HV9lyftjMH9+TuyUOYlBJ7zLrodSQxF0J0F4qisGTJEj799FP27duH2Wzm9NNP529/+xuhoaGNHvfee+/x+uuvU1RUxIABA3jooYcYMWKEz3kXL17MRx99RG5uLmazmbFjx/K3v/2N3r17M2nSJGbOnMl9993nPWbSpEk4HA7Wrl3r3fbSSy/x4Ycf8tNPPzX4O7ympobnn3+eZcuWUVpaSlRUFBMmTOCee+4hIiKCK6+80jvtIDU1tdG5xOAZQa9LlpxOJ//85z/58MMPeeihh7j44ovrtXc4HFRVVeF2u6mtrcVoNKJWNzy+Fx4e3uA+u92O0Wist91oNOJwONi1a5dPvx5LcXExF1xwAeHh4Vx88cVERkaSn5/PihUr0Gg03nYrVqzgzjvvpFevXlx33XWYzWYyMjJ8PswsXryYp59+mtNPP517772X/Px83n77bYqKiliyZIm3XWZmJiEhIcyZM4eLLrqIM888k969ewPwySef8OCDDzJu3DjuuOMOrFYr7777LldffTVff/11vffucDgoKytrsi/rNNanO3bsIDk5meDgYJ/tw4YN8+5PSEhoZo+2D4fDwTfffEN6ejqJiYl+jaWzkQTdD+Yt2wzUT5Dzyveg15qICu7h3aZRa3G63HySsZ/nvt/u8+CnTq3isvTe3HvaMAbFNV3VRQghjseGDRuO2ebIOa82m42MjIwm2+v1em8yAVBVVUVmZmazzt9Sixcv5pVXXuGuu+5i+vTp5OTk8Mgjj5CTk+OThB3p999/x+Vy8corr+BwOHjooYe4+eab+e677zCbPc/0vPTSS7z22mvcc889nHzyyRQXF/Poo49y1VVX8dVXXzF+/Hifvtu7dy9VVVVotVp2795Nv379AM/o64QJExodYJk/fz6rVq3iueeeIyUlhYMHDzJ37lweeughFi5cyMKFC7n22mvR6/UsXLiQkJCQBs9TWFhIeXk5/fv3p7y8nNtvv52dO3eyePFixo4d22g/zJ49u1n9/N133zWYiPXp04c//vgDl8vlTaDtdjtbtnimWhYUFDTr/HU+//xzampqWLFihU+Ceuutt3pfZ2dnc8899zB58mSeffZZn6kodrvnW+cNGzbwzDPPMGfOHG677Tbv/piYGB599FG2bNnCsGHDKCkpoaSkBKvVyn//+1/69u3rbZuVlcVDDz3Erbfeyi233OLdPnHiRM455xx+/PFHpk6d6hP/pk2buPrqq5v1Xhvr06KiogZHteu2FRYWNuv87ennn3+mvLxcpre0giToHezoVT3rkvS88j1sObAKlUrDyOTpRAX3oNrm4K1GHvy8fkwKp0S6mXjCUO8fCiHE8UtNTeXrr7/2vhYeRycYR1OpVJSU/DmAUFRUdMxjevXqxebNm70/b9u2jRkzZjTa/ugpB83lcDhYsmQJZ599Nn/5y18ASEpK4oEHHmDOnDn8/vvvnHDCCfWOs1gsPPPMM97E7u9//zuzZs1izZo1TJkyBbvdzttvv82FF17IVVddBUBycjL//Oc/ueCCC1i5ciXjx4/n888/x2KxYDab+fXXXxk+3PN7f926dfTr1w+r1crmzZuZNWtWo+/hzjvv5JZbbqFXr14AJCQkcMYZZ/Dee++hKArh4eFotVp0Ol2TUxHqPgCpVCouvPBCdDodH374oXckuCEDBw7kzTffxOVy4XA40Ol0PqPUR2rs2rNmzeKRRx7hwQcf5Prrr8ftdvPKK69QVFQEQG1tbaPXb0hlZSVOp5Nt27Y1+sHipZdewmAw8OSTT9abJ67X6wF45ZVXiIuLY86cOT77R44cCXg+UA0bNszbbzfddJNPcg54H5689NJLfe7R2NhYdDodBw4cqBdbamoqr7zySpN9WaexPq2trfW+jyPVvdeW9ml7+PLLL9HpdJxxxhn+DqXTkQS9A3mS880khtoI1jt549ffAIW/jA1hy4FVKCho1RrKrQrP/7iJV39p+MHPa8b0ReVysGPHDv+9GSG6qJCQEE488UR/hyHa0N69e6murmbcuHE+2+v+nbdv395ggp6WluaT2NV9YNu7d6/3/2tqauqN7A8ePBiDwcD27du58cYbcblcbNq0ifHjx/Prr796269fv57LL7/cO1J/dHxHUqvVvPPOO/z4448UFxd7k2WHw4Hdbm/2g4p1FVwee+wx0tLSWLRoUZNTfADCwsIYN24cLpfLO8XlWEnl0S677DLy8/NZvHixtwxfWloa1113Ha+++ipBQUEtOt+5557Lf//7X2bPns2QIUOYMWMGZ599NrGxsYBnhHz16tVceOGF9aaA1LFarfz6669cffXV9d5PXSnjugS4rt+O/gBpt9v54YcfsFqt9aqX1GnovYWFhTF27NhW9WUdo9Ho/SbgSDabzbvfn2pqavjuu++YMGGCz7MHonkkQe8g85Zt5v2Na7lhVBnhRod3e3XNF3y+WU/vyAhqnWq+yuzB6799j93l9rYZ3SuKu08ZwnlpvdBqPPPQLBZHvWsIIUR7Wb58eYvax8TEHPOYo0f/hgwZ0uLrNEfdirAPPfQQ8+bNq7e/bhT3aEcnrnXfVtYlb3XnPXo6iVqtxmw2U1NTQ2RkJIMHD2b9+vWMGzeO3377jSuuuAK32+2tsvHrr7+SlpbWaBKjKArXXXcdeXl5zJ071/vB4Z133jlmxZajZWVl0bNnT3r16sWuXbuwWCzHTNDtdjsVFRW4XC5v1ZLGksrIyMhG9915551ce+217Nq1i5CQEFJTU3nuuecAzzcPLZGcnMyyZctYsWIFq1at4sUXX+Tll1/m9ddfZ+TIkRw4cACr1UpaWlqj59i3bx9Op5M+ffrU21f3cGfdaHlmZiYxMTHebzDq1F3n9ttvb3QO/cCBA+tts9vtFBcXN9mXdRrr05iYmAanBtXdz3UfVvxl5cqVUr3lOEiC3gHqkvNzBhWhQvFuN+ncRJvtOF1W1u938K918RTW5Hv3nzk4kbsnD2ZiCx78FEKI9tDS+d8Gg6HFx4SEhBzXPPPG1CWgd999N5MnT27wug05urJIXWJeNyJad96qqiqfdm63m5qaGu95J0yYwIYNG8jMzMRisTBixAjcbjfl5eXs2bPHO/+8MVlZWezcuZN58+b5PPjZ0OjpsWRlZTFw4EDmz5/PBRdcwJw5c/jPf/7T5Aj8pk2bjnsOep2wsDCff+NffvmF+Pj4VtVBDwoK4txzz+Xcc89l7969nH322XzzzTeMHDmyRdM7Gpom8t///peePXt6a8VnZmY2mGjX3SN9+/Zt8huQo/3xxx/HPQd94MCBrFu3jurqap9vCeqmjfl7AagvvvgCs9nMqaee6tc4OitJ0NtZ3bSWG0aVNZicA7gVFcUWKKzRo9eouXJUCneePFge/BTCD7Zs2cKVV14JwDvvvOPzEKPonPr06UNISAgHDx70mWvtcrnIyckhMjKyweO2bNnindIBnjnygLdEYd15169f71NCbuvWrdjtdoYOHQrA+PHjeeutt/jhhx8YPny4NyFMS0vjhx9+YNu2bcydO7fR+B0OzzemR8ZZXV3t/bZBUf7823Lk66O5XC727NnDySefTGRkJAsXLvTODX/iiScaPa4t5qA35OuvvyYjI4P77rvvmJVMjlRaWlrv38xoNOJyubyjxnX/zmvXruXcc8/1aVv3HpKSktBqtfz++++cffbZPnGtX7+ehx9+GJVKhcvlYvfu3Q1+SOnZsycqlYrly5czbdo0n31Op5OamhrCwur/LW+LOejTp0/njTfeYOnSpd466Ha7nU8++YThw4f7VHCxWq0cOnSIiIiIRu/3tlRaWsratWuZOXMmJpOp3a/XFUmC3o7qHghNDLX5TGvRqRWf5LyoRo9J6yYx1MbF6ek8c3bbjyAJIZrHbrd7H+pqzQilCDxarZarrrqKRYsW0atXLyZNmoTVauXNN99k5cqVfPPNNw2ucGg0GnnwwQe58cYbqa2t5fHHHycuLs47UqrT6bjmmmt45ZVXGDBgAJMmTeLgwYM89thjpKSkcPrppwOexVs0Gg3vv/++zwIvo0aN4t1338VsNnsfHG1ISkoKYWFhvPfee/Tv35+KigqefPJJTj/9dD766CPWrVvHmDFjCA0NZefOnWRkZBAdHV2vxF5OTg42m807KpyWlsYjjzzC/fffz5AhQ7jiiisavH5bzEFfv349L7/8MuPHjyc8PJzNmzfzySefMHHixHqJ77vvvsu7777L559/3uDo9vz589m1axennnoqiYmJFBYW8uGHHxIfH+8tExkcHMz555/PJ598gt1uZ8yYMdTU1LBu3TomT57M5Zdfjtls5pJLLuGDDz5Aq9WSmprK5s2b+fjjjznnnHO8D+3W9VtDI+hRUVHMnDmTL7/8kurqaiZNmoTL5WL//v0sX76c5557rsFvhdpiDvrw4cOZPn06zz33HCUlJfTu3Zv//e9/5Obm+tRnB8+HzdmzZ/PXv/7Vp9LNu+++S2Vlpbfiy+rVq8nP93yTf+WVV/p8u9SStl9//TVOp1OmtxwHSdA7QLDe6fOzw62i0qYlRO+iqEaP3eWZvhKkdzW6sJAQQojWu/baawkLC+P999/n6aefRq/XM3r0aN57771Glx+fMGECAwYM4IYbbqCkpIRBgwbx6quv+kwHueWWWzAYDLz99tv885//JCQkhIkTJ3LPPfd4k0u9Xs/YsWNZvXo1Y8aM8R47atQoXnvtNaZNm9ZkkmY2m1mwYAFPPPEE55xzDr179+aOO+4gPT2dTZs2cdttt/Gvf/2La665hnvvvZdZs2Zx1113cc011/icp+5Bx7oEHeD8888nIyODJ598ktTUVEaPHt3yzm2GuLg4NBoNixcvpqamhsTERO644w6uvvpqtFrfVKSsrIzs7OxGzzVu3DhKSkr48MMPqa6uJiEhgSlTpnDLLbcQHh7ubffQQw8RGxvLt99+y8qVKwkLC+OEE05g4sSJ3jb33nsvKpWKL7/8ko8++ojk5GQeeughLr30Um+bun5rrKrTE088wYABA/j88895+umnMRqN9OrVi4suuoghQ4a0prua7emnn+aFF17g888/p6KigtTUVF599dVm/zu+8cYb5Ob+ua7K8uXLvd/MnH322T5Jd0vafvHFF0RFRbVo2o/wpVKa+j6sm9i4cSPwZ1mltjRv2Wbe+PU3LhtWf0UvjVrB5f5zbnlo0CQemDq5Wee1WCzs2LGDQYMGSZnF4yR92Xa6Ql9u2LDBWx5w+fLl7TInujk6ui+tVitAl/w6+nhGfsWfOrIfp0yZwooVK9r1Gv4k92TDjvV7qD3ztUAjI+jtzFPnXCGv9BufaS6AT3KeGBHDnJNP7uDohBBCiMDy888/t/vIsxCBrvlPZYhWe3jaCJKjR6HQcCWW5MhgLkqfLpVahBBCdHsTJkzghRde8HcYQviVJOgdZO6UKUQEn0iZ1XeOeWJEDOeNOI+4sPp1WIUQQgghRPcjCXoHmjtlCj2izuD9LQl8vjOW0KBJzDn5JknOhRBCCCGEl8xB72APTxsBh6e6PDC18bJaQgghhBCie5IE3Q88D44KIQLR0KFD2bJlC9CyRVeEEEKItiIJuhBCHMFgMDS5VHlXpdFoZGEmIYRfuVyuBhen6o5kDroQQgh0Oh1Wq7XJpeKFEKK9KIqC1WpFp5MFG0FG0IUQQgAqlYqIiAiKi4sxmUxdavEUt9uNzWYDQK2WcanWkn5sO9KXvlwuF1arlYiICCk5fZjcFUIIcYQNGzYQFRVFVFQUGzZs8Hc4HUqr1RIdHd3lvmK22+1kZ2fLFJ7jJP3YdqQvfen1eqKjo9FqZdy4jvSEEEIcpTtP81CpVF0uQa/79zQYDI0uIS6OTfqx7UhfimOREXQhhBBCCCECiCToQgghhBBCBBBJ0IUQQgghhAggkqALIYQQQggRQCRBF0IIIYQQIoBIgi6EEEIIIUQAUSnduZ7YYRs3bvR3CEKIAKEoCi6XCwCNRiOLZgghRIAZOXKkv0Nod1IHXQghjqBSqWSxDCGEEH4lI+hCCCGEEEIEEJmDLoQQQgghRACRBF0IIYQQQogAIgm6EEIIIYQQAUQSdCGEEEIIIQKIJOhCCCGEEEIEEEnQhRBCCCGECCCSoAshhBBCCBFAJEEXQgghhBAigEiCLoQQQgghRACRBD2A5ebmMmfOHMaOHcu4ceOYO3culZWV9dp98sknDBw4kKFDh/r8b8uWLX6IOjClpqaSlpbm0z+PPfZYg22XLFnCtGnTOOGEE7jsssvYunVrB0cbuNavX1/vPktLSyM1NbVe24ULFzJo0KB67YuLi/0QeWD46aefGDduHHfeeWe9fV9//TVnnXUW6enpnH/++fz888+Nnqe8vJw77riDcePGMWHCBB588EFqa2vbM/SA01RfLl++nLPPPpv09HSmTZvGhx9+2Oh5rrzySoYMGeJzj5599tntGXrAaawvW/q3Re7Lxvvy73//e71+HDx4MPfff3+D5zn11FPr/c266aabOuItiACh9XcAonE33XQTaWlprFq1iqqqKubMmcNTTz3F448/Xq/t6NGjeeedd/wQZefx7bffkpiY2GSbVatWsXDhQl5//XVSU1NZsmQJN910E8uXL8dsNndQpIFr9OjRZGRk+Gx79dVX2blzZ4PtzznnHJ588smOCC3gvfbaa/z3v/+ld+/e9fbt2LGD++67j//7v//jxBNPZNmyZfz1r3/l22+/JT4+vl77f/zjH9jtdr788kscDge33347CxYs4O9//3tHvBW/a6ovt2zZwt/+9jeee+45Jk+ezJo1a5gzZw4pKSmMGjWqwfM99thjnH/++e0ddkBqqi+hZX9b5L5svC/nz5/P/PnzvT87nU7OPfdcpk+f3uj5Fi9ezNixY9slVhH4ZAQ9QFVWVpKWlsbdd99NUFAQ8fHxnHfeeWzYsMHfoXVpS5cu5fzzz2f48OEYjUauv/56AFavXu3nyALToUOHePPNN7n33nv9HUrAMxgMjf7x/uijjzj55JM5+eSTMRgMnH322QwYMIDPP/+8Xtvi4mJWrlzJnXfeSWRkJHFxcdxyyy18/PHHOByOjngrftdUX5aXl3PjjTdy+umno9VqOfnkkxkwYID87mxEU33ZEnJftqwv3377bXr06MHJJ5/cAZGJzkgS9AAVGhrKE088QXR0tHdbXl4esbGxDbbPy8vjmmuuYfTo0Zx22ml89tlnHRVqp/Hss88yefJkRo0axT/+8Q9qamrqtdm2bRuDBw/2/qxWqxk0aFC9UWPh8eKLL3LBBRfQo0ePBvdnZmZy6aWXcsIJJzBz5swmp210dbNnzyYkJKTBfUffdwCDBw9u8L7bsWMHGo3GZ1rRkCFDsFgs7N27t22DDlBN9eWkSZOYM2eO92en00lRURFxcXGNnu/rr79mxowZpKenc/XVV7N///42jzlQNdWX0Py/LXJfHrsv61RWVvLqq69yzz33NNluyZIlnH766aSnp3PbbbdRUlLSVqGKTkAS9E4iIyODd999l5tvvrnevsjISJKTk7nnnntYs2YNd911Fw888ABr1671Q6SBacSIEYwbN47ly5ezdOlS/vjjD+bNm1evXXl5OWFhYT7bwsLCKCsr66hQO42DBw+yfPlyrrnmmgb3x8fH06tXL5566inWrFnDRRddxE033dRt/li3REvuu/LycoKDg1GpVD5tAblPG7BgwQLMZjMzZsxocH/fvn3p378///nPf/juu++IjIzk+uuvx263d3Ckgaclf1vkvmy+d999l9GjR9O/f/9G2wwaNIhhw4bx2Wef8fXXX1NeXs7tt9/egVEKf5M56J3Axo0bufnmm7n77rsZN25cvf2TJ09m8uTJ3p9nzpzJihUr+OSTTzjppJM6MNLAtXTpUu/rvn378re//Y2bb76Z+fPno9frfdoqitLR4XVK7733HlOnTiUmJqbB/RdddBEXXXSR9+err76ar776is8//5w77rijg6LsPFpy38k9emyKorBgwQK+/PJLlixZgsFgaLDdI4884vPzo48+ytixY9m4cWO3//3Z0r8tcl8em8vl4r333uPZZ59tst3LL7/sfR0UFMTDDz/MjBkz2L9/P0lJSe0dpggAMoIe4FatWsVf/vIXHnjgAWbPnt3s43r27ElhYWE7Rta5JSYm4nK56n1lGBERQXl5uc+28vJyIiMjOzC6zmHZsmWceuqpLTpG7suGteS+i4yMpLq6GpfL5dMWICoqqj3D7DTcbjdz585l1apVvP/++6SkpDT72ODgYMLCwigoKGjHCDuvxv4blvuyedavX4/dbm/0geXG9OzZE0B+f3YjkqAHsN9//5377ruPF198kXPPPbfRdu+//z5ff/21z7Y9e/bQq1evdo6wc9i+fXu9SiJ79uxBr9fXm9OflpbGtm3bvD+7XC62b9/O8OHDOyTWzmLHjh3k5uYyfvz4Rtv861//qvdVuNyXDUtLS6tXzjMjI6PB+27QoEEoiuJTOScjI4PQ0FD69OnT7rF2Bv/85z/ZtWsX77//fpP3W3V1NY888ohPMl5aWkppaancp7Tsb4vcl83z3XffceKJJ6LVNj6BITc3l4cffthnmtWePXsA5L7sRiRBD1BOp5O///3v/O1vf2PChAn19l911VXeX5x2u53HHnuMjIwMHA4HX375JT/++COXXnppR4cdkKKioli6dCmLFi3CbreTnZ3Niy++yCWXXIJGo2H69OneCg+XXXYZn376KX/88QdWq5VXXnkFvV7v8zWv8HzoCQ8PJzg42Gf7kX1ZXl7OvHnz2Lt3LzabjTfeeIP9+/dz3nnn+SPkgHbxxRfzyy+/8P3332Oz2fjvf/9LTk6Otx73ihUrmDVrFuAZqZw2bRovvPACpaWl5Ofn8/LLL3PhhRc2+Ue/u9i4cSOff/45ixYtIjw8vN7+LVu2MH36dOx2O8HBwWzevJn58+dTXl5ORUUF8+bNIzU1lfT09I4PPsAc62+L3Jctt2PHjgbL/R7Zl1FRUaxatYonn3wSi8VCQUEBTzzxBKecckqTDzuLrkX+qwlQf/zxB3v27KlXOxU89bwPHDhARUUF4HlyvKamhttvv52ioiISExN5+eWXSUtL80foAScuLo5Fixbx7LPPehPu8847z7uQRHZ2NhaLBfBUgLjrrru44447KCkpYejQoSxatAij0ejPtxBwiouLG5x7fmRf3n333YBn7nl5eTn9+vXjrbfearCud3cwdOhQwPPhG2DlypWAZ5RxwIABLFiwgCeeeILc3Fz69evHv//9b28fV1VVsW/fPu+5Hn30UR5++GFOO+00dDodZ555ZoML9nRVTfXlxx9/TFVVFaeccorPMaNHj+aNN97AarWSnZ3tnS/98ssv889//pNp06Zht9s56aSTWLRoEWp19xi/aqovj/W3Re5LX031ZZ2ioiKf6mx1juxLo9HI66+/zpNPPsmkSZMAmDJlSqOLGomuSaXIUx1CCCGEEEIEjO4xRCCEEEIIIUQnIQm6EEIIIYQQAUQSdCGEEEIIIQKIJOhCCCGEEEIEEEnQhRBCCCGECCCSoAshhBBCCBFAJEEXQgghhBAigEiCLoQQXUhqairvv/++v8MQQghxHCRBF0IIIYQQIoBIgi6EEEIIIUQAkQRdCCG6sJdeeokJEyawf/9+f4cihBCimbT+DkAIIUT7ePfdd/nPf/7Du+++S1JSkr/DEUII0UySoAshRBf01Vdf8dJLL/Hmm2/Sr18/f4cjhBCiBSRBF0KILmbt2rWsWrWKO+64gyFDhvg7HCGEEC0kc9CFEKKL+fXXX5k2bRqvvfYahw4d8nc4QgghWkgSdCGE6GJuv/12nnnmGQYNGsQdd9yBw+Hwd0hCCCFaQBJ0IYToYtRqNWq1mgULFpCbm8uCBQv8HZIQQogWkARdCCG6qOjoaBYsWMA777zDypUr/R2OEEKIZlIpiqL4OwghhBBCCCGEh4ygCyGEEEIIEUAkQRdCCCGEECKASIIuhBBCCCFEAJEEXQghhBBCiAAiCboQQgghhBABRBJ0IYQQQgghAogk6EIIIYQQQgQQSdCFEEIIIYQIIJKgCyGEEEIIEUAkQRdCCCGEECKASIIuhBBCCCFEAJEEXQghhBBCiADy/wr3uBiXWdQ2AAAAAElFTkSuQmCC\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["CPU times: user 1min 2s, sys: 20.8 s, total: 1min 23s\n","Wall time: 54.4 s\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":99}]},{"cell_type":"code","source":["visualizer.elbow_value_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KTDgH6N97bpH","executionInfo":{"status":"ok","timestamp":1700671635887,"user_tz":-60,"elapsed":223,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"179f0809-2297-4816-9dcd-e1ac2aa7b563"},"execution_count":100,"outputs":[{"output_type":"execute_result","data":{"text/plain":["9"]},"metadata":{},"execution_count":100}]},{"cell_type":"code","source":["kmeans = KMeans(n_clusters=visualizer.elbow_value_, random_state=1)\n","elbow_labels_pred = kmeans.fit_predict(X)"],"metadata":{"id":"dQPdTW5kWKm3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700671656074,"user_tz":-60,"elapsed":1308,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"1827432e-59b6-4e4d-a817-2d36b7525927"},"execution_count":101,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n","\n","The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n","\n"]}]},{"cell_type":"code","source":["score_clustering(got.Name, elbow_labels_pred)"],"metadata":{"id":"5MuecsmRWMOy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700671656074,"user_tz":-60,"elapsed":3,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"588e9fcd-5809-4b67-884f-dbd47cb14e3d"},"execution_count":102,"outputs":[{"output_type":"stream","name":"stdout","text":["Rand index : 0.941421568627451\n","Adjusted Mutual Info : 0.8460179684139897\n","Homogeneity : 0.787453689258317\n","Completeness : 0.9508273370293835\n","V measure : 0.8614631156510449\n","Fowlkes Mallows : 0.7511879341377521\n"]}]},{"cell_type":"markdown","metadata":{"id":"Z4fR_IhSNz0j"},"source":["# Topic Modelling"]},{"cell_type":"code","metadata":{"id":"c5Pg5COUN6Fq","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1700671768246,"user_tz":-60,"elapsed":45069,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"5fcc852c-ced3-445d-927c-fe3175ba015d"},"source":["!pip install pyldavis\n","!pip install pandas==1.5.3\n","!gdown 'https://drive.google.com/uc?id=1Ase_bvYHC_B4ngJu6fDxQ5jHrwM4mvul'"],"execution_count":103,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting pyldavis\n"," Downloading pyLDAvis-3.4.1-py3-none-any.whl (2.6 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting numpy>=1.24.2 (from pyldavis)\n"," Downloading numpy-1.26.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.2/18.2 MB\u001b[0m \u001b[31m49.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from pyldavis) (1.11.3)\n","Collecting pandas>=2.0.0 (from pyldavis)\n"," Downloading pandas-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.3/12.3 MB\u001b[0m \u001b[31m62.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from pyldavis) (1.3.2)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from pyldavis) (3.1.2)\n","Requirement already satisfied: numexpr in /usr/local/lib/python3.10/dist-packages (from pyldavis) (2.8.7)\n","Collecting funcy (from pyldavis)\n"," Downloading funcy-2.0-py2.py3-none-any.whl (30 kB)\n","Requirement already satisfied: scikit-learn>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from pyldavis) (1.2.2)\n","Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (from pyldavis) (4.3.2)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from pyldavis) (67.7.2)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->pyldavis) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->pyldavis) (2023.3.post1)\n","Collecting tzdata>=2022.1 (from pandas>=2.0.0->pyldavis)\n"," Downloading tzdata-2023.3-py2.py3-none-any.whl (341 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.8/341.8 kB\u001b[0m \u001b[31m28.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->pyldavis) (3.2.0)\n","Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim->pyldavis) (6.4.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->pyldavis) (2.1.3)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=2.0.0->pyldavis) (1.16.0)\n","Installing collected packages: funcy, tzdata, numpy, pandas, pyldavis\n"," Attempting uninstall: numpy\n"," Found existing installation: numpy 1.23.5\n"," Uninstalling numpy-1.23.5:\n"," Successfully uninstalled numpy-1.23.5\n"," Attempting uninstall: pandas\n"," Found existing installation: pandas 1.5.3\n"," Uninstalling pandas-1.5.3:\n"," Successfully uninstalled pandas-1.5.3\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","lida 0.0.10 requires fastapi, which is not installed.\n","lida 0.0.10 requires kaleido, which is not installed.\n","lida 0.0.10 requires python-multipart, which is not installed.\n","lida 0.0.10 requires uvicorn, which is not installed.\n","cupy-cuda11x 11.0.0 requires numpy<1.26,>=1.20, but you have numpy 1.26.2 which is incompatible.\n","google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.1.3 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed funcy-2.0 numpy-1.26.2 pandas-2.1.3 pyldavis-3.4.1 tzdata-2023.3\n","Collecting pandas==1.5.3\n"," Downloading pandas-1.5.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.1 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.1/12.1 MB\u001b[0m \u001b[31m23.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3) (2023.3.post1)\n","Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3) (1.26.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas==1.5.3) (1.16.0)\n","Installing collected packages: pandas\n"," Attempting uninstall: pandas\n"," Found existing installation: pandas 2.1.3\n"," Uninstalling pandas-2.1.3:\n"," Successfully uninstalled pandas-2.1.3\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","lida 0.0.10 requires fastapi, which is not installed.\n","lida 0.0.10 requires kaleido, which is not installed.\n","lida 0.0.10 requires python-multipart, which is not installed.\n","lida 0.0.10 requires uvicorn, which is not installed.\n","pyldavis 3.4.1 requires pandas>=2.0.0, but you have pandas 1.5.3 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed pandas-1.5.3\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["pandas"]}}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading...\n","From: https://drive.google.com/uc?id=1Ase_bvYHC_B4ngJu6fDxQ5jHrwM4mvul\n","To: /content/papers.csv\n","100% 184M/184M [00:01<00:00, 125MB/s]\n"]}]},{"cell_type":"code","metadata":{"id":"tFLnqI-4N17F","colab":{"base_uri":"https://localhost:8080/","height":293},"executionInfo":{"status":"ok","timestamp":1700671774476,"user_tz":-60,"elapsed":2546,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"03778c74-e26a-44c5-ca56-b26002f55487"},"source":["# Importing modules\n","import pandas as pd\n","import os\n","import re\n","from wordcloud import WordCloud\n","\n","\n","# Read data into papers\n","papers = pd.read_csv('/content/papers.csv')\n","# Print head\n","papers.head()"],"execution_count":104,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" id year title event_type \\\n","0 1 1987 Self-Organization of Associative Database and ... NaN \n","1 10 1987 A Mean Field Theory of Layer IV of Visual Cort... NaN \n","2 100 1988 Storing Covariance by the Associative Long-Ter... NaN \n","3 1000 1994 Bayesian Query Construction for Neural Network... NaN \n","4 1001 1994 Neural Network Ensembles, Cross Validation, an... NaN \n","\n"," pdf_name abstract \\\n","0 1-self-organization-of-associative-database-an... Abstract Missing \n","1 10-a-mean-field-theory-of-layer-iv-of-visual-c... Abstract Missing \n","2 100-storing-covariance-by-the-associative-long... Abstract Missing \n","3 1000-bayesian-query-construction-for-neural-ne... Abstract Missing \n","4 1001-neural-network-ensembles-cross-validation... Abstract Missing \n","\n"," paper_text \n","0 767\\n\\nSELF-ORGANIZATION OF ASSOCIATIVE DATABA... \n","1 683\\n\\nA MEAN FIELD THEORY OF LAYER IV OF VISU... \n","2 394\\n\\nSTORING COVARIANCE BY THE ASSOCIATIVE\\n... \n","3 Bayesian Query Construction for Neural\\nNetwor... \n","4 Neural Network Ensembles, Cross\\nValidation, a... "],"text/html":["\n","
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idyeartitleevent_typepdf_nameabstractpaper_text
011987Self-Organization of Associative Database and ...NaN1-self-organization-of-associative-database-an...Abstract Missing767\\n\\nSELF-ORGANIZATION OF ASSOCIATIVE DATABA...
1101987A Mean Field Theory of Layer IV of Visual Cort...NaN10-a-mean-field-theory-of-layer-iv-of-visual-c...Abstract Missing683\\n\\nA MEAN FIELD THEORY OF LAYER IV OF VISU...
21001988Storing Covariance by the Associative Long-Ter...NaN100-storing-covariance-by-the-associative-long...Abstract Missing394\\n\\nSTORING COVARIANCE BY THE ASSOCIATIVE\\n...
310001994Bayesian Query Construction for Neural Network...NaN1000-bayesian-query-construction-for-neural-ne...Abstract MissingBayesian Query Construction for Neural\\nNetwor...
410011994Neural Network Ensembles, Cross Validation, an...NaN1001-neural-network-ensembles-cross-validation...Abstract MissingNeural Network Ensembles, Cross\\nValidation, a...
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yeartitleabstractpaper_text
62911993Inverse Dynamics of Speech Motor ControlAbstract MissingInverse Dynamics\\nof Speech Motor Control\\n\\nM...
53072015Parallel Predictive Entropy Search for Batch G...We develop \\textit{parallel predictive entropy...Parallel Predictive Entropy Search for Batch G...
33062010A VLSI Implementation of the Adaptive Exponent...We describe an accelerated hardware neuron bei...A VLSI Implementation of the Adaptive Exponent...
20372005Maximum Margin Semi-Supervised Learning for St...Abstract MissingMaximum Margin Semi-Supervised\\nLearning for S...
51682015Learning with a Wasserstein LossLearning to predict multi-label outputs is cha...Learning with a Wasserstein Loss\\nCharlie Frog...
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\n"]},"metadata":{},"execution_count":105}]},{"cell_type":"code","metadata":{"id":"JBcbSKj-Solq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700671906148,"user_tz":-60,"elapsed":226,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"398982b9-aa95-4649-e11d-81942d7ef026"},"source":["# Remove punctuation\n","papers['paper_text_processed'] = \\\n","papers['paper_text'].map(lambda x: re.sub('[,\\.!?]', '', x))\n","# Convert the titles to lowercase\n","papers['paper_text_processed'] = \\\n","papers['paper_text_processed'].map(lambda x: x.lower())\n","# Print out the first rows of papers\n","papers['paper_text_processed'].head()"],"execution_count":106,"outputs":[{"output_type":"execute_result","data":{"text/plain":["6291 inverse dynamics\\nof speech motor control\\n\\nm...\n","5307 parallel predictive entropy search for batch g...\n","3306 a vlsi implementation of the adaptive exponent...\n","2037 maximum margin semi-supervised\\nlearning for s...\n","5168 learning with a wasserstein loss\\ncharlie frog...\n","Name: paper_text_processed, dtype: object"]},"metadata":{},"execution_count":106}]},{"cell_type":"code","metadata":{"id":"wZhaMpIiSx0O","colab":{"base_uri":"https://localhost:8080/","height":217},"executionInfo":{"status":"ok","timestamp":1700672042401,"user_tz":-60,"elapsed":3837,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"b504fc5c-a770-4bfd-87b8-3b40922658fc"},"source":["# Join the different processed titles together.\n","long_string = ','.join(list(papers['paper_text_processed'].values))\n","\n","# Create a WordCloud object\n","wordcloud = WordCloud(background_color=\"red\", max_words=5000, contour_width=3, contour_color='steelblue')\n","\n","# Generate a word cloud\n","wordcloud.generate(long_string)\n","\n","# Visualize the word cloud\n","wordcloud.to_image()"],"execution_count":109,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""],"image/png":"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\n"},"metadata":{},"execution_count":109}]},{"cell_type":"code","metadata":{"id":"dM9uKXipS-r0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700672088932,"user_tz":-60,"elapsed":854,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"52606a71-b524-42ca-b797-4fad20677678"},"source":["import gensim\n","from gensim.utils import simple_preprocess\n","import nltk\n","nltk.download('stopwords')\n","from nltk.corpus import stopwords"],"execution_count":111,"outputs":[{"output_type":"stream","name":"stderr","text":["[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data] Package stopwords is already up-to-date!\n"]}]},{"cell_type":"code","metadata":{"id":"h76MIXH_S_bX","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700672092239,"user_tz":-60,"elapsed":3309,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"a3c393a3-abc6-4e26-b103-fc3dfe077e86"},"source":["stop_words = stopwords.words('english')\n","stop_words.extend(['from', 'subject', 're', 'edu', 'use'])\n","def sent_to_words(sentences):\n"," for sentence in sentences:\n"," # deacc=True removes punctuations\n"," yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))\n","def remove_stopwords(texts):\n"," return [[word for word in simple_preprocess(str(doc))\n"," if word not in stop_words] for doc in texts]\n","data = papers.paper_text_processed.values.tolist()\n","data_words = list(sent_to_words(data))\n","# remove stop words\n","data_words = remove_stopwords(data_words)\n","print(data_words[:1][0][:30])"],"execution_count":112,"outputs":[{"output_type":"stream","name":"stdout","text":["['inverse', 'dynamics', 'speech', 'motor', 'control', 'makoto', 'hirayama', 'eric', 'vatikiotis', 'datesol', 'mitsuo', 'kawato', 'atr', 'human', 'information', 'processing', 'research', 'laboratories', 'hikaridai', 'seika', 'cho', 'soraku', 'gun', 'kyoto', 'japan', 'abstract', 'progress', 'made', 'computational', 'implementation']\n"]}]},{"cell_type":"code","metadata":{"id":"y9vx5iAXTEf4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700672147717,"user_tz":-60,"elapsed":875,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"45e69d67-9651-46b3-eeb6-0894bf84a9c5"},"source":["import gensim.corpora as corpora\n","\n","# Create Dictionary\n","id2word = corpora.Dictionary(data_words)\n","\n","# Create Corpus\n","texts = data_words\n","\n","# Term Document Frequency\n","corpus = [id2word.doc2bow(text) for text in texts]\n","\n","# View\n","print(corpus[:1][0][:30])"],"execution_count":113,"outputs":[{"output_type":"stream","name":"stdout","text":["[(0, 1), (1, 2), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 18), (8, 2), (9, 3), (10, 1), (11, 4), (12, 1), (13, 1), (14, 4), (15, 1), (16, 2), (17, 2), (18, 1), (19, 1), (20, 1), (21, 1), (22, 5), (23, 2), (24, 3), (25, 1), (26, 1), (27, 3), (28, 1), (29, 4)]\n"]}]},{"cell_type":"code","metadata":{"id":"_MuyggAtTTdT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700672196947,"user_tz":-60,"elapsed":2712,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"5e20c161-0a62-44bd-d2f3-5e19da650438"},"source":["# number of topics\n","num_topics = 10\n","# Build LDA model\n","lda_model = gensim.models.LdaMulticore(corpus=corpus,\n"," id2word=id2word,\n"," num_topics=num_topics)\n","# Print the Keyword in the 10 topics\n","for t in lda_model.print_topics():\n"," print(t)\n","doc_lda = lda_model[corpus]"],"execution_count":114,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:gensim.models.ldamulticore:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n"]},{"output_type":"stream","name":"stdout","text":["(0, '0.006*\"model\" + 0.006*\"learning\" + 0.005*\"data\" + 0.005*\"set\" + 0.005*\"one\" + 0.004*\"function\" + 0.004*\"kernel\" + 0.004*\"figure\" + 0.004*\"network\" + 0.004*\"features\"')\n","(1, '0.010*\"data\" + 0.006*\"learning\" + 0.005*\"model\" + 0.004*\"set\" + 0.004*\"number\" + 0.004*\"figure\" + 0.004*\"one\" + 0.004*\"algorithm\" + 0.004*\"two\" + 0.004*\"function\"')\n","(2, '0.007*\"set\" + 0.006*\"data\" + 0.005*\"learning\" + 0.005*\"function\" + 0.004*\"model\" + 0.004*\"features\" + 0.004*\"using\" + 0.004*\"algorithm\" + 0.003*\"number\" + 0.003*\"models\"')\n","(3, '0.007*\"model\" + 0.006*\"learning\" + 0.005*\"data\" + 0.004*\"time\" + 0.004*\"one\" + 0.004*\"training\" + 0.004*\"kernel\" + 0.003*\"network\" + 0.003*\"figure\" + 0.003*\"neural\"')\n","(4, '0.006*\"learning\" + 0.006*\"model\" + 0.004*\"data\" + 0.004*\"function\" + 0.004*\"using\" + 0.004*\"time\" + 0.004*\"one\" + 0.004*\"set\" + 0.004*\"neural\" + 0.004*\"network\"')\n","(5, '0.008*\"model\" + 0.006*\"data\" + 0.005*\"learning\" + 0.005*\"using\" + 0.004*\"two\" + 0.004*\"set\" + 0.004*\"function\" + 0.004*\"models\" + 0.004*\"number\" + 0.003*\"one\"')\n","(6, '0.009*\"learning\" + 0.007*\"data\" + 0.006*\"model\" + 0.006*\"set\" + 0.004*\"function\" + 0.004*\"one\" + 0.004*\"problem\" + 0.004*\"figure\" + 0.004*\"training\" + 0.004*\"algorithm\"')\n","(7, '0.008*\"data\" + 0.006*\"model\" + 0.006*\"using\" + 0.005*\"learning\" + 0.005*\"kernel\" + 0.004*\"algorithm\" + 0.004*\"set\" + 0.004*\"one\" + 0.004*\"number\" + 0.004*\"function\"')\n","(8, '0.006*\"model\" + 0.005*\"data\" + 0.005*\"set\" + 0.004*\"learning\" + 0.004*\"algorithm\" + 0.004*\"problem\" + 0.003*\"distribution\" + 0.003*\"using\" + 0.003*\"time\" + 0.003*\"models\"')\n","(9, '0.007*\"model\" + 0.005*\"figure\" + 0.005*\"learning\" + 0.005*\"algorithm\" + 0.004*\"data\" + 0.004*\"time\" + 0.004*\"two\" + 0.004*\"using\" + 0.004*\"function\" + 0.004*\"set\"')\n"]}]},{"cell_type":"code","metadata":{"id":"8dZ2jJcfTiCk","executionInfo":{"status":"ok","timestamp":1700672232411,"user_tz":-60,"elapsed":234,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"source":["import pyLDAvis.gensim_models as gensimvis\n","import pickle\n","import pyLDAvis"],"execution_count":115,"outputs":[]},{"cell_type":"code","metadata":{"id":"2MJtdfG_TyVk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700672259988,"user_tz":-60,"elapsed":9728,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"d5542847-cf69-48c2-b553-99e10dc2c788"},"source":["# Visualize the topics\n","pyLDAvis.enable_notebook()\n","LDAvis_data_filepath = os.path.join('/content/ldavis_prepared_'+str(num_topics))\n","# # this is a bit time consuming - make the if statement True\n","# # if you want to execute visualization prep yourself\n","if 1 == 1:\n"," LDAvis_prepared = gensimvis.prepare(lda_model, corpus, id2word)\n"," with open(LDAvis_data_filepath, 'wb') as f:\n"," pickle.dump(LDAvis_prepared, f)\n","\n","# load the pre-prepared pyLDAvis data from disk\n","with open(LDAvis_data_filepath, 'rb') as f:\n"," LDAvis_prepared = pickle.load(f)\n","pyLDAvis.save_html(LDAvis_prepared, '/content/ldavis_prepared_'+ str(num_topics) +'.html')\n"],"execution_count":116,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning:\n","\n","`should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n","\n"]}]},{"cell_type":"code","metadata":{"id":"pEGnWNgoVF3_","colab":{"base_uri":"https://localhost:8080/","height":951},"executionInfo":{"status":"ok","timestamp":1700672289972,"user_tz":-60,"elapsed":525,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"a3fb140f-72bf-4d3c-ee72-221cc5452276"},"source":["LDAvis_prepared"],"execution_count":117,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning:\n","\n","`should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n","\n"]},{"output_type":"execute_result","data":{"text/plain":["PreparedData(topic_coordinates= x y topics cluster Freq\n","topic \n","6 0.005282 -0.002009 1 1 24.632668\n","1 -0.001958 -0.009000 2 1 14.146308\n","7 0.002898 -0.001655 3 1 13.336632\n","9 -0.009402 0.002549 4 1 12.136504\n","0 0.003209 0.001227 5 1 10.540251\n","3 -0.003238 -0.000515 6 1 6.504113\n","2 0.004605 0.005311 7 1 5.898710\n","5 -0.001304 0.001831 8 1 5.631916\n","8 0.000791 -0.002342 9 1 5.339156\n","4 -0.000883 0.004604 10 1 1.833742, topic_info= Term Freq Total Category logprob loglift\n","348 model 1347.000000 1347.000000 Default 30.0000 30.0000\n","302 learning 1418.000000 1418.000000 Default 29.0000 29.0000\n","514 set 1045.000000 1045.000000 Default 28.0000 28.0000\n","622 using 852.000000 852.000000 Default 27.0000 27.0000\n","142 data 1488.000000 1488.000000 Default 26.0000 26.0000\n","... ... ... ... ... ... ...\n","209 figure 10.693457 824.879555 Topic10 -5.9519 -0.3468\n","674 algorithms 8.029594 416.582451 Topic10 -6.2384 0.0499\n","231 given 8.814111 583.531073 Topic10 -6.1451 -0.1939\n","265 information 8.128401 458.801339 Topic10 -6.2261 -0.0344\n","1364 results 8.300943 579.401334 Topic10 -6.2051 -0.2468\n","\n","[861 rows x 6 columns], token_table= Topic Freq Term\n","term \n","7266 1 0.228086 abdel\n","7266 3 0.228086 abdel\n","7266 8 0.228086 abdel\n","12163 1 0.154938 acnn\n","12163 2 0.154938 acnn\n","... ... ... ...\n","5966 6 0.073406 yt\n","5966 7 0.064230 yt\n","5966 8 0.100933 yt\n","5966 9 0.045878 yt\n","5966 10 0.018351 yt\n","\n","[3886 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[7, 2, 8, 10, 1, 4, 3, 6, 9, 5])"],"text/html":["\n","\n","\n","\n","
\n",""]},"metadata":{},"execution_count":117}]},{"cell_type":"markdown","metadata":{"id":"xv5G2Gi1v6VR"},"source":["#Summarization"]},{"cell_type":"code","metadata":{"id":"FkV9Sf77v7uk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638377050251,"user_tz":-60,"elapsed":32,"user":{"displayName":"Gian Carlo Milanese","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08352423327035271918"}},"outputId":"69c33fd2-46cf-4ea4-8c8f-a018829b75ff"},"source":["import numpy as np\n","import pandas as pd\n","import nltk\n","nltk.download('punkt') # one time execution\n","nltk.download('stopwords')\n","import re"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data] Package punkt is already up-to-date!\n","[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data] Package stopwords is already up-to-date!\n"]}]},{"cell_type":"code","metadata":{"id":"gvgsxV0ZwXlk"},"source":["DOCUMENT = \"\"\"\n","Italy, officially the Italian Republic (Italian: Repubblica Italiana [reˈpubblika itaˈljaːna]),[13][14] is a country consisting of a peninsula delimited by the Alps and several islands surrounding it,[15] whose territory largely coincides with the homonymous geographical region.[16] Italy is located in the centre of the Mediterranean Sea, in Southern Europe,[17][18][19] and is also considered part of Western Europe.[20][21] A unitary parliamentary republic with Rome as its capital and largest city, the country covers a total area of 301,340 km2 (116,350 sq mi) and shares land borders with France, Switzerland, Austria, Slovenia, and the enclaved microstates of Vatican City and San Marino. Italy has a territorial exclave in Switzerland (Campione) and a maritime exclave in Tunisian waters (Lampedusa). With around 60 million inhabitants, Italy is the third-most populous member state of the European Union. Due to its central geographic location in Southern Europe and the Mediterranean, Italy has historically been home to myriad peoples and cultures. In addition to the various ancient peoples dispersed throughout what is now modern-day Italy, the most predominant being the Indo-European Italic peoples who gave the peninsula its name, beginning from the classical era, Phoenicians and Carthaginians founded colonies mostly in insular Italy,[22] Greeks established settlements in the so-called Magna Graecia of Southern Italy, while Etruscans and Celts inhabited central and northern Italy respectively. An Italic tribe known as the Latins formed the Roman Kingdom in the 8th century BC, which eventually became a republic with a government of the Senate and the People. The Roman Republic initially conquered and assimilated its neighbours on the Italian peninsula, eventually expanding and conquering parts of Europe, North Africa and Asia. By the first century BC, the Roman Empire emerged as the dominant power in the Mediterranean Basin and became a leading cultural, political and religious centre, inaugurating the Pax Romana, a period of more than 200 years during which Italy's law, technology, economy, art, and literature developed.[23][24] During the Early Middle Ages, Italy endured the fall of the Western Roman Empire and barbarian invasions, but by the 11th century numerous rival city-states and maritime republics, mainly in the northern and central regions of Italy, became prosperous through trade, commerce, and banking, laying the groundwork for modern capitalism.[25] These mostly independent statelets served as Europe's main trading hubs with Asia and the Near East, often enjoying a greater degree of democracy than the larger feudal monarchies that were consolidating throughout Europe; however, part of central Italy was under the control of the theocratic Papal States, while Southern Italy remained largely feudal until the 19th century, partially as a result of a succession of Byzantine, Arab, Norman, Angevin, Aragonese, and other foreign conquests of the region.[26] The Renaissance began in Italy and spread to the rest of Europe, bringing a renewed interest in humanism, science, exploration, and art. Italian culture flourished, producing famous scholars, artists, and polymaths. During the Middle Ages, Italian explorers discovered new routes to the Far East and the New World, helping to usher in the European Age of Discovery. Nevertheless, Italy's commercial and political power significantly waned with the opening of trade routes that bypassed the Mediterranean.[27] Centuries of foreign meddling and conquest, and the rivalry and infighting between the Italian city-states, such as the Italian Wars of the 15th and 16th centuries, left Italy politically fragmented, and it was further conquered and divided among multiple foreign European powers over the centuries. By the mid-19th century, rising Italian nationalism and calls for independence from foreign control led to a period of revolutionary political upheaval. After centuries of foreign domination and political division, Italy was almost entirely unified in 1861 following a war of independence, establishing the Kingdom of Italy.[28] From the late 19th century to the early 20th century, Italy rapidly industrialised, mainly in the north, and acquired a colonial empire,[29] while the south remained largely impoverished and excluded from industrialisation, fuelling a large and influential diaspora.[30] Despite being one of the victorious allied powers in World War I, Italy entered a period of economic crisis and social turmoil, leading to the rise of the Italian fascist dictatorship in 1922. Participation in World War II on the Axis side ended in military defeat and economic destruction during the Italian campaign. Following the rise of the Italian Resistance and the liberation of Italy, the country abolished its monarchy, established a democratic Republic, enjoyed a prolonged economic boom, and became a highly developed country.[31] Italy has an advanced economy. The country is the eighth-largest by nominal GDP (third in the European Union), the sixth-largest by national wealth and the third-largest by central bank gold reserve. It ranks highly in life expectancy, quality of life,[32] healthcare,[33] and education. The country is a great power and it has a significant role in regional[34][35] and global [36][37] economic, military, cultural, and diplomatic affairs. Italy is a founding and leading member of the European Union and a member of numerous international institutions, including the United Nations, NATO, the OECD, the Organization for Security and Co-operation in Europe, the World Trade Organization, the Group of Seven, the G20, the Union for the Mediterranean, the Latin Union, the Council of Europe, Uniting for Consensus, the Schengen Area, and many more. The source of many inventions and discoveries, the country has long been a global centre of art, music, literature, philosophy, science and technology, and fashion, and has greatly influenced and contributed to diverse fields including cinema, cuisine, sports, jurisprudence, banking, and business.[38] As a reflection of its cultural wealth, Italy has the world's largest number of World Heritage Sites (58), and is the fifth-most visited country.\n","\"\"\""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"UZtNEtgExC_f","colab":{"base_uri":"https://localhost:8080/","height":122},"executionInfo":{"status":"ok","timestamp":1638377050252,"user_tz":-60,"elapsed":24,"user":{"displayName":"Gian Carlo Milanese","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08352423327035271918"}},"outputId":"95ad18e1-3d9d-4044-a860-a91544bb1ddc"},"source":["\n","DOCUMENT = re.sub(r'\\n|\\r', ' ', DOCUMENT)\n","DOCUMENT = re.sub(r' +', ' ', DOCUMENT)\n","DOCUMENT = re.sub(r'\\d+', ' ', DOCUMENT)\n","pattern = r'\\[[^\\]]*\\]'\n","DOCUMENT = re.sub(pattern, ' ', DOCUMENT)\n","DOCUMENT = DOCUMENT.strip()\n","DOCUMENT"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["\"Italy, officially the Italian Republic (Italian: Repubblica Italiana ), is a country consisting of a peninsula delimited by the Alps and several islands surrounding it, whose territory largely coincides with the homonymous geographical region. Italy is located in the centre of the Mediterranean Sea, in Southern Europe, and is also considered part of Western Europe. A unitary parliamentary republic with Rome as its capital and largest city, the country covers a total area of , km ( , sq mi) and shares land borders with France, Switzerland, Austria, Slovenia, and the enclaved microstates of Vatican City and San Marino. Italy has a territorial exclave in Switzerland (Campione) and a maritime exclave in Tunisian waters (Lampedusa). With around million inhabitants, Italy is the third-most populous member state of the European Union. Due to its central geographic location in Southern Europe and the Mediterranean, Italy has historically been home to myriad peoples and cultures. In addition to the various ancient peoples dispersed throughout what is now modern-day Italy, the most predominant being the Indo-European Italic peoples who gave the peninsula its name, beginning from the classical era, Phoenicians and Carthaginians founded colonies mostly in insular Italy, Greeks established settlements in the so-called Magna Graecia of Southern Italy, while Etruscans and Celts inhabited central and northern Italy respectively. An Italic tribe known as the Latins formed the Roman Kingdom in the th century BC, which eventually became a republic with a government of the Senate and the People. The Roman Republic initially conquered and assimilated its neighbours on the Italian peninsula, eventually expanding and conquering parts of Europe, North Africa and Asia. By the first century BC, the Roman Empire emerged as the dominant power in the Mediterranean Basin and became a leading cultural, political and religious centre, inaugurating the Pax Romana, a period of more than years during which Italy's law, technology, economy, art, and literature developed. During the Early Middle Ages, Italy endured the fall of the Western Roman Empire and barbarian invasions, but by the th century numerous rival city-states and maritime republics, mainly in the northern and central regions of Italy, became prosperous through trade, commerce, and banking, laying the groundwork for modern capitalism. These mostly independent statelets served as Europe's main trading hubs with Asia and the Near East, often enjoying a greater degree of democracy than the larger feudal monarchies that were consolidating throughout Europe; however, part of central Italy was under the control of the theocratic Papal States, while Southern Italy remained largely feudal until the th century, partially as a result of a succession of Byzantine, Arab, Norman, Angevin, Aragonese, and other foreign conquests of the region. The Renaissance began in Italy and spread to the rest of Europe, bringing a renewed interest in humanism, science, exploration, and art. Italian culture flourished, producing famous scholars, artists, and polymaths. During the Middle Ages, Italian explorers discovered new routes to the Far East and the New World, helping to usher in the European Age of Discovery. Nevertheless, Italy's commercial and political power significantly waned with the opening of trade routes that bypassed the Mediterranean. Centuries of foreign meddling and conquest, and the rivalry and infighting between the Italian city-states, such as the Italian Wars of the th and th centuries, left Italy politically fragmented, and it was further conquered and divided among multiple foreign European powers over the centuries. By the mid- th century, rising Italian nationalism and calls for independence from foreign control led to a period of revolutionary political upheaval. After centuries of foreign domination and political division, Italy was almost entirely unified in following a war of independence, establishing the Kingdom of Italy. From the late th century to the early th century, Italy rapidly industrialised, mainly in the north, and acquired a colonial empire, while the south remained largely impoverished and excluded from industrialisation, fuelling a large and influential diaspora. Despite being one of the victorious allied powers in World War I, Italy entered a period of economic crisis and social turmoil, leading to the rise of the Italian fascist dictatorship in . Participation in World War II on the Axis side ended in military defeat and economic destruction during the Italian campaign. Following the rise of the Italian Resistance and the liberation of Italy, the country abolished its monarchy, established a democratic Republic, enjoyed a prolonged economic boom, and became a highly developed country. Italy has an advanced economy. The country is the eighth-largest by nominal GDP (third in the European Union), the sixth-largest by national wealth and the third-largest by central bank gold reserve. It ranks highly in life expectancy, quality of life, healthcare, and education. The country is a great power and it has a significant role in regional and global economic, military, cultural, and diplomatic affairs. Italy is a founding and leading member of the European Union and a member of numerous international institutions, including the United Nations, NATO, the OECD, the Organization for Security and Co-operation in Europe, the World Trade Organization, the Group of Seven, the G , the Union for the Mediterranean, the Latin Union, the Council of Europe, Uniting for Consensus, the Schengen Area, and many more. The source of many inventions and discoveries, the country has long been a global centre of art, music, literature, philosophy, science and technology, and fashion, and has greatly influenced and contributed to diverse fields including cinema, cuisine, sports, jurisprudence, banking, and business. As a reflection of its cultural wealth, Italy has the world's largest number of World Heritage Sites ( ), and is the fifth-most visited country.\""]},"metadata":{},"execution_count":71}]},{"cell_type":"code","metadata":{"id":"GPctlXllyIpk"},"source":["sentences = nltk.sent_tokenize(DOCUMENT)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"gPfLImIcyJiq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638377050254,"user_tz":-60,"elapsed":23,"user":{"displayName":"Gian Carlo Milanese","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08352423327035271918"}},"outputId":"c8536754-51bf-4fcb-c77f-39f9cbbf7c3b"},"source":["import numpy as np\n","\n","stop_words = nltk.corpus.stopwords.words('english')\n","\n","def normalize_document(doc):\n"," # lower case and remove special characters\\whitespaces\n"," doc = re.sub(r'[^a-zA-Z\\s]', '', doc, re.I|re.A)\n"," doc = doc.lower()\n"," doc = doc.strip()\n"," # tokenize document\n"," tokens = nltk.word_tokenize(doc)\n"," # filter stopwords out of document\n"," filtered_tokens = [token for token in tokens if token not in stop_words]\n"," # re-create document from filtered tokens\n"," doc = ' '.join(filtered_tokens)\n"," return doc\n","\n","normalize_corpus = np.vectorize(normalize_document)\n","norm_sentences = normalize_corpus(sentences)\n","norm_sentences[:3]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['italy officially italian republic italian repubblica italiana country consisting peninsula delimited alps several islands surrounding whose territory largely coincides homonymous geographical region',\n"," 'italy located centre mediterranean sea southern europe also considered part western europe',\n"," 'unitary parliamentary republic rome capital largest city country covers total area km sq mi shares land borders france switzerland austria slovenia enclaved microstates vatican city san marino'],\n"," dtype='"]},"metadata":{},"execution_count":76}]},{"cell_type":"code","metadata":{"id":"f37Ya1-5zGAE","colab":{"base_uri":"https://localhost:8080/","height":357},"executionInfo":{"status":"ok","timestamp":1638377051252,"user_tz":-60,"elapsed":671,"user":{"displayName":"Gian Carlo Milanese","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08352423327035271918"}},"outputId":"ded0dbba-d356-4fe7-9361-f1fa5dabfc7a"},"source":["import matplotlib.pyplot as plt\n","%matplotlib inline\n","\n","plt.figure(figsize=(12, 6))\n","networkx.draw_networkx(similarity_graph, node_color='lime')"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAqwAAAFUCAYAAADh627ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3zN1x/H8ddNEImRRCJEqE0iVkhC7F17/dRo7KJWUbMxSgchtEaUVhU1o6WNlCo1ilIZxKqQ2sSIEdn7nt8fkVuRIYks+nnmkUe43/M95/sNrnfO9wyNUgohhBBCCCEKKr38vgAhhBBCCCEyIoFVCCGEEEIUaBJYhRBCCCFEgSaBVQghhBBCFGgSWIUQQgghRIEmgVUIIYQQQhRohTI6aG5uripVqpRHlyKEEEIIIf6rTp069UgpVTqtYxkG1kqVKuHn55c7VyWEEEIIIcQzGo3mZnrHZEiAEEIIIYQo0CSwCiGEEEKIAk0CqxBCCCGEKNAksAohhBBCiAJNAqsQQgghhCjQJLAKIYQQQogCTQKrEEIIIYQo0CSwCiGEEEKIAk0CqxBCCCGEKNAksAohhBBCiAJNAqsQQgghhCjQJLAKIQqcxMREYmJiSExMzO9LEUIIUQAUyu8LEEL8d8XHx3Po0CGOHz9OQEAAAQEBXLlyhbi4OJRSaDQaChcuTNWqVbGxscHGxgYnJyfat29PkSJF8vvyhRBC5BHpYRVC5Llz584xbtw4rKys+Pjjj1FK0bt3bzZv3szjx49JTExEKYVWq+Xp06d4eHjQt29fChUqhKurK+XKlWPUqFH4+fnl960IIYTIAxqlVLoH7e3tlfyHIITIKQ8fPmT27Nl4enrywQcfMGDAAKpWrZrlem7evImHhwfLly+nQ4cOuLq6YmlpmQtXLIQQIq9oNJpTSin7tI5JD6sQIk8cOHAAW1tbDA0NuXTpErNnz85WWAWoWLEiM2bM4PLly5QtW5Y6deqwe/fuHL5iIYQQBYUEViFErtu6dSvOzs7s2LGDZcuWYWpqmiP1lihRgoULF/Lrr78ycuRI1q5dmyP1CiGEKFhk0pUQIlft2bOHadOmcfDgQWrXrp0rbTg6OnL06FHat29PyZIl6du3b660I4QQIn9ID6sQItfcvXuX9957Dw8Pj1wLq8mqV6/Ozp07GT9+PNevX8/VtoQQQuQtCaxCiFwzceJE3n//fZo3b54n7TVs2JDp06czZsyYPGlPCCFE3pDAKoTIFVevXuXw4cNMnz49T9udMGEC586d49y5c3narhBCiNwjgVUIkSu++uorRo4cSbFixfK03SJFijB+/Hjc3d3ztF0hhBC5RwKrECJXHDp0iF69euVL2z179uTQoUP50rYQQoicJ4FVCJHjIiMjCQwMpF69evnSfs2aNXn48CGPHj3Kl/aFEELkLAmsQogcFxQURLly5TAwMMiX9vX19alatSo3b97Ml/aFEELkLAmsQohcoaeXv28v+d2+EEKInCPv6EKIHFeoUCFiY2Pz9RpiY2PR19fP12sQQgiRMySwCiFyXMWKFQkJCSEkJCRf2o+OjubatWvUrFkzX9oXQgiRsySwCiFynL6+Pg0aNMDb2ztf2j916hQ2NjYYGhrmS/tCCCFyVqH8vgAhxJupR48ebNiwgY4dO2bpvGCC2cAGznGOUEIxxpi61GUYwyhN6UzV8f3339O9e/fsXLYQQogCSKOUSvegvb298vPzy8PLEUK8KcLCwqhcuTKnT5+mYsWKLy3viy+uuLKXvQDEEKM7ZoghCkUnOuGCCw44pFtPcHAwNWvWJDAwkNKlMxdwhRBC5D+NRnNKKWWf1jEZEiCEyBUlS5Zk3LhxjBs3jox+MAZYzWpa0QpPPIl59vG8aKKJIQZPPGlFK1azOs16lFJMmDCBoUOHSlgVQog3iARWIUSumT17NsHBwSxfvjzdMqtZzVSmEkUUioyDrUIRRRRTmZpmaF23bh1///03CxYseOVrF0IIUXBIYBVC5JoiRYrg4eHBokWL+Pbbb1Md98WXiSsnEmUfBQbA0OcOXgTsAdNnn+2evQa60OrHv0OWtmzZwsyZM9m+fbtMthJCiDeMBFYhRK6qUqUKx44dY+HChcyZM4f4+HjdMVdciS8XD7OB4S+cWA7YATwBHgHdgf7/Ho4mGldcSUxMZOHChcycOZNDhw5Rq1at3L4lIYQQeUwCqxAi11WrVo0TJ07g7e1N/fr1+f333wkmOGmCVW+gJ2D2wkkmQCVAAyhAH7jy72GFYnfibuq1q8evv/7K8ePHsbW1zZP7EUIIkbdkWSshRJ4oU6YM+/btw8vLi9GjR5MwOYGEUQlQ+CUnmgARgBb4NOWhuLg46i+tz6Z6m9BoNLlz4UIIIfKd9LAKIfKMRqOhR48eBAQEUKVHFRIKJ7z8pKdAKLASsHvhmCHo1deTsCqEEG846WEVQuS5IkWKULx88cyfUAwYDZQGAgCLfw+FkD/bvwohhMg70sMqhMgXxhhn7QQtEAUEpXzZFNOcuiQhhBAFlARWIUS+qEtdilIUEoAYIPHZZwxJr/0O+D97LQyYTNLyVjb/1mGIIXWok7cXLoQQIs9JYBVC5IuhyYuufg4YAguBzc9+/TlJY1cHAMZAVeAq8BtQ9N86FOrfeoQQQryxZAyrECJfWGCB/UN7/pzzJ8xLp9A76Z+vQUNnOlMa2YJVCCHedNLDKoTIcwkJCcydO5eAQQEYKINs1WGIIS645PCVCSGEKIgksAoh8tTt27dp06YNx48f5/z68ywttJTCcS9bjDUlI4xYwhLssc+lqxRCCFGQSGAVQuSZXbt2YW9vT6dOndi3bx+WlpY0OdsEzTQNmihN0koAGdAoDUTBhJsTGMOYvLloIYQQ+U7GsAohcl1MTAzTpk3jl19+4eeff6ZJkya61/v160fhO4Ux/NsQh50OHCx6ED30SDRI1J1viCEKRWdNZxz/cuSrYV8x0WciZcuWza9bEkIIkYcksAohctWlS5fo378/1atX58yZM5iYmOiOzZo1i8jISJo1a4aBgQE/Gv6ISXUTbBfboldPj8jCkcQ/jGdEoxEMZWjSBKu2ED08mt69e3P48GEMDLI3BlYIIcTrQ4YECCFyhVKK9evX07x5c8aOHcsPP/yQIqweOnSIjRs3AhAQEICLiwtnz56lukl1quyswof+H7L8+nLKTi/LNKalWA3g448/ply5crz//vsopfL83oQQQuQtCaxCiBwXFhbGwIEDWbJkCYcPH2bUqFFoNBrd8ZCQEIYMGYKBgQF9+vShatWqNG7cGG9vbxo1akRISAimpqbY2try999/pwqlenp6fP/995w9e5Yvv/wyr29PCCFEHpPAKoTIUb6+vjRo0IDixYvj6+tL7dq1U5UZN24cVlZW2NnZsW/fPmbOnAnAyZMnady4sS6wlilTBqUUwcHBqeooVqwYu3bt4osvvmDv3r25fl9CCCHyjwRWIUSO0Gq1LFmyhC5duuDq6so333yDkZFRqnLbtm3Dx8eHwMBAunTpQrFixWjbti1Aqh5WjUaj62VNy1tvvcWPP/7IkCFDCAgIyNX7E0IIkX8ksAohXllwcDBdunRh586d+Pj48M47aW9Rdfv2bSZOnEj58uWZNGkS69atY+bMmWg0Gh49ekRwcDDW1ta6wApkGFgBmjZtyqJFi+jevTtPnjzJlfsTQgiRvySwCiFeycGDB7Gzs8POzo6jR49SqVKlNMtptVqGDBlCx44dCQoKwt7enoiICHr06AGAj48PDg4OaDQaQkNDdRO0XhZYAYYNG0a3bt3o168fCQkJOXp/Qggh8p8EViFEtsTHxzNz5kwGDx7M999/z4IFCyhcOP0dq5YtW0Z0dDTHjh1j5cqVfPHFF8yYMQM9vaS3oZMnT9KoUSPCw8MxMjKiUKGkVfcyE1gB3Nzc0NfXZ/LkyTlzg0IIIQoMCaxCiCy7ceMGLVu25PTp0/j7+9OuXbsMy1+4cAFXV1ccHR1p2LAhJiYmXLlyhXfffVdXxtvbm8aNG/PkyRPdcAAg3ZUCXlSoUCE8PDzYv38/33777avdoBBCiAJFAqsQIkt27NiBo6MjvXv35tdff8XCwiLD8rGxsTg7OzNlyhS2bNnC0qVLcXV1Zdq0aboeWa1Wi4+PT4oJV8ksLCzQ19fn/v37L702ExMTvLy8mD17NkePHn21GxVCCFFgyE5XQohMiY6O5sMPP+T3339n9+7dODo6Zuq8OXPmUKVKFY4cOcL06dMJDQ3l5MmTbNu2TVcmMDAQU1NTLCwsOH/+PKVKlUpRR3Ivq6Wl5Uvbq1GjBps2baJfv3789ddf6Y6pFUII8fqQHlYhxEv9/fffODg4EBYWhr+/f6bD6pEjR9i8eTM9evTg5s2bTJo0iUWLFjFx4kQMDQ115ZKXswJS9bBC5sexJuvQoQMfffQR3bt3Jzw8PNPnCSGEKJgksAoh0qWUYs2aNbRs2VL3SL9kyZKZOjc0NJTBgwfj7u7O3LlzWbVqFUFBQezdu5exY8emKJu8YQDkTGAFmDBhAo6OjgwaNAitVpulc4UQQhQsEliFEGl6+vQp/fr146uvvuLYsWMMGzYsxfaqLzN+/Hg6d+6Mn58fzZo1o1WrVixevJhRo0ZhbGycomxO97ACaDQaVq1axePHj/n444+zdK4QQoiCRcawCiFS+euvv3j33Xfp0qULGzdupGjRolk6/4cffsDb2xsPDw86dOjA+fPnuX//Ph4eHly6dClF2aioKC5fvoydnR2QcWBVSmUpNBcpUoSdO3fi6OhI7dq16d+/f5buQwghRMEgPaxCCB2tVourqys9e/Zk6dKlrFy5MsthNSgoiPHjx7Np0yamTZvG7NmzsbS0ZOnSpTg7O6daVeDUqVPUrl0bAwMDIO3Aam5ujoGBAXfv3s3yPVlYWLBr1y4++OAD/Pz8sny+EEKI/Cc9rEIIAO7fv8+gQYOIiYnBz8+PChUqZLkOrVbLsGHDGD9+PNevX+fRo0eMHz+ekJAQ1q5dy+nTp1Odk7xhQLK0Aiv828tqZWWV5euqV68ea9asoVevXvj4+GRqtQEhhBAFh/SwCiH47bffsLOzo0mTJhw+fDhbYRVg5cqVhIeHM378eKZMmcKqVasoVKgQX331Fd26daNixYqpzkneMCDZywJrdvXq1YtRo0bRs2dPYmJisl2PEEKIvCeBVYj/sLi4OKZNm8bIkSPZtm0bn3zyiW5L1Ky6ePEin332GZs2bWL+/Pl06NCBpk2bEhUVhbu7OzNmzEjzvKz2sL6K2bNnU6lSJUaOHPnSnbOEEEIUHBJYhfiPunr1Ks2aNePSpUv4+/vTqlWrbNcVFxeHs7MzCxYsIDo6mk2bNrFo0SIA1q5dS7NmzbCxsUl1XlBQELGxsVSpUkX3Wm4GVo1Gw/r167l48SKLFy9+pbqEEELkHQmsQvwHbdu2jcaNGzNw4EC8vLwwNzd/pfrmzp1L+fLlee+99xg3bhyffPIJFhYWxMXFsWTJElxcXNI8L3k5q+dn/mcUWC9evPjKPaNGRkZ4enqyfPlyfvnll1eqSwghRN6QSVdC/IdERkYyYcIEjh07xr59+2jQoMEr13ns2DE2bNjAmTNn2Lx5M9HR0YwaNQqALVu2YG1tjb29fZrnPr9hACRN2goNDcXExCRV2VKlSmFkZMSdO3eyPcY2WYUKFdi5cyfdunXjjz/+wNbW9pXqE0IIkbukh1WI/4izZ89ib29PQkICp06dypGwGhYWxuDBg1mzZg0GBgbMmDGDVatWoa+vT2JiIgsXLky3dxVSbhgAEB4ejpGRUbrjaGvVqvXKwwKSNW7cmC+++ILu3bvz+PHjHKlTCCFE7pDAKsQbTinFypUradeuHTNnzuT777+nRIkSOVL3hAkTaN++Pd26dWP27Nn06NEDBwcHAH7++WdMTU3THRubkJDA6dOncXR01L2W3nCAZMnDAnLK4MGD6d27N3369CE+Pj7H6hVCCJGzZEiAEG+wJ0+eMHz4cG7fvs2JEyeoXr16jtW9c+dOjh8/jr+/P6dPn2bHjh26MKmUYsGCBcybNy/dnakuXLhAhQoVUmzTmpnA6uPjk2P3ALBw4UK6d+/OxIkTWbVqVY7WLYQQImdID6sQb6hjx45hZ2dH5cqVczys3rt3j7Fjx7Jp0yaMjIwYO3YsCxYsoFSpUgDs37+fuLg4unbtmm4dLy5nBZkLrDk1JCCZvr4+W7du5Y8//mD16tU5WrcQQoicIT2sQrxhEhMTmT9/PqtWreK7776jS5cuOVq/Uorhw4czevRoGjduzLfffouenh5Dhw7VlXF1dcXFxQU9vfR/Jn5xwwDI/JAApVS6PbfZYWxsjJeXF02bNsXa2prWrVvnWN1CCCFenfSwCvEGuXPnDm3btuXw4cOcOnUqx8MqwKpVq3j8+DGzZ8/WfV21apUunJ44cYJbt27Rr1+/DOvJTg+rqakpxYsX5/bt269+Iy+oVq0aW7duZcCAAVy9ejXH6xdCCJF9EliFeEPs3r0be3t72rVrx4EDB7CyssrxNi5dusTcuXPZvHkzhQsXxsXFhX79+lG/fn1dGVdXV6ZPn57hjllPnz7lzp071K5dO8XrLwuskDvDApK1bduW2bNn0717d8LCwnKlDSGEEFknQwKEeM3FxsYyY8YMfv75Z3bs2EGzZs1ypZ24uDgGDhzIZ599Ro0aNfD29mb37t0EBAToypw7d45Tp07x448/ZliXr68vDRo0SBVqsxJYO3XqlP2bycC4ceM4f/48zs7OeHp6oq+vnyvtCCGEyDzpYRXiNRYYGIiTkxO3bt3C398/18IqwKeffkqZMmUYPXo0iYmJjB07Fjc3txSz/BcuXMikSZMoWrRohnW9uGFAsvzuYYWk7Vvd3d0JCwtj1qxZudaOEEKIzJMeViFeUxs3bmTKlCl8+umnjB49OkcnIb3oxIkTrF27ljNnzqDRaPj6668pUaIEzs7OujJXrlxh//79fP311y+tz9vbm+HDh6d6PbOB9dtvv836TWRBkSJF2LlzJ46OjtSuXZuBAwfmantCCHHt2jUOHTpEQEAAAQEBXL58mbCwMB49egSApaUllSpVwsbGBhsbGxo2bEiLFi3+M0+BJLAK8ZoJDw9n7NixnDp1ioMHD1K3bt1cb2/QoEF8/fXXlC1blgcPHjBv3jz++OOPFCF58eLFjBkzhpIlS2ZYn1KKkydPsmbNmlTHnjx58tLAWqtWLQICAtBqtRmuQvCqzM3N2bVrF23atKF69eqpJogJIcSrCgkJYePGjWzdupXr16/TsWNHbG1tadmyJTVr1sTU1JRChQqhlCIqKorr16/rAu22bdsICgqib9++DB06NEd2LyzIJLAK8Ro5deoUAwYMoEWLFvj6+lKsWLFcb3PSpEm0atWKnj17AjB9+nSGDh2Kra2trszdu3f58ccfCQwMfGl9165dw9DQkHLlyqU6lpkeVhMTE4yNjbl16xaVKlXK2s1kUZ06dVi7di3/+9//8Pb2zpWJbEKI/56EhAS+/fZb5s2bR7t27fjkk09o27YthQsXTvccMzMzKlSoQIsWLXSvBQYG4uHhQbdu3WjTpg2LFi1K8731TSCBVYjXgFKKZcuWsWDBAtzd3enfv3+etOvp6ckff/zBmTNngKTNCJIfWT3vyy+/ZPDgwZibm7+0zrSWs0oWEhKi23wgI8njWHM7sAL06NGDv//+m549e3L06FEMDQ1zvU0hxJvr9u3b9OzZkxIlSrB//37q1auX7bpq1KjBxx9/zIcffoirqyt169bF3d2dAQMG5OAVFwwy6UqIAu7hw4d07doVDw8PvL298yys3r9/n9GjR7Np0yZKlChBfHw8Y8eO5csvv6R48eK6ck+ePGH9+vVMmTIlU/WmtWFAssz0sELuT7x6kYuLC9WrV2f48OEopfKsXSHEm+XChQs0bdqU/v37c/jw4VcKq88rUaIECxYs4PDhw8yYMYMvvvgiR+otSCSwClGAHT58GDs7O2rXrs2xY8eoUqVKnrSrlOK9995jxIgRNGnSBAB3d3fKli1Lnz59UpR1d3enZ8+eVKhQIVN1p9fDqtVqCQ0NxcTE5KV15HVg1Wg0fPfdd1y5cgVXV9c8a1cI8ea4efMm7dq1w9XVlWnTpuXKRNk6depw/PhxvvvuO1asWJHj9ecnGRIgRAGUkJDAJ598wnfffcf69et5++2387T9b775hvv37/Pxxx8DSWNUFyxYwPHjx1O8yUZERPDVV1/x559/ZqremJgY/v77bxo2bJjqWHh4OEZGRhluOJDM1taW1atXZ/JucoahoSGenp40atQIW1tbevToke26lFI8fPiQgIAArl+/TmRkJIcPH6Z9+/aUKFGCGjVqULNmTUqUKJGDdyCEyC8JCQm8++67TJ48OcXqKrmhQoUK7N69m0aNGtG0adM0329fRxJYhShgbt26xbvvvouRkRGnT5+mbNmyedp+YGAgs2fP5tixYxQpUgSAKVOm8P7771OzZs0UZdesWUOrVq2oUaNGpuo+c+YMNWvWxMjIKNWxzA4HgKSVAi5dupTrKwW8yMrKip9++okuXbpQpUoV6tSpk+lznz59iqenJ9u3b8fHxwelFDY2NlStWpXChQuzf/9+zMzMePr0KZcvXyYwMBAzMzNatWrFgAEDaN++fYYTMoQQBdeyZcsoVqwYU6dOzZP2qlSpwooVKxg8eDDnz5/P0/fJXKOUSvezYcOGSgiRd3766SdlYWGhFi5cqBITE/O8/bi4OOXg4KDc3d11rx04cEBVrFhRRUZGpigbExOjrKys1OnTpzNd/9KlS9WYMWPSPHb69GlVr169TNdVoUIFdfXq1UyXz0mbN29WlStXVsHBwS8te/PmTeXs7KxKliypevbsqbZv367u37+vtFpthuclJiaqK1euqBUrVignJydlbm6u5syZoyIiInLqNoQQeSA2NlaVK1dOnTlzJk/b1Wq1qmHDhsrLyytP230VgJ9KJ5O+AZFbiNdfdHQ048aNY/LkyezatYsZM2bky0/En3/+OaVKlWLcuHFA0nas48ePZ/ny5al6RTdu3EidOnWws7PLdP3e3t4ZrhCQ2R5WyPtxrM9zdnamb9++9OnTh7i4uDTLxMbG8sknn9CgQQOqVavGzZs3+fnnn+nbty9lypR56fg1PT09qlatygcffMCJEyfw9vbm6tWrWFtb4+HhkRu3JYTIBTt27MDGxibHJlhllkajYfLkySxdujRP280tEliFyGcBAQE0atSIR48e4e/vn+4M+tx28uRJvv76a9atW6cLU19++SVVq1ale/fuKcomJibi5uaGi4tLltt41RUCkuVnYAWYP38+JUuW5IMPPki1ckBoaCidOnXC19eX06dPM2/evExNJstIlSpV2LJlCx4eHsybN49p06ah1WpfqU4hRO7bv38/77zzTr603atXL06ePEl0dHS+tJ+TJLAKkU+UUqxdu5bmzZszYcIEPDw8XjnUZFdERAQDBw5k1apVukWnb926xZIlS1ixYkWq3sAdO3ZQpkwZmjdvnuk2Hjx4wNOnT6levXqax1+3wKqvr8+WLVs4fvw4X331le714OBgWrRoQa1atdi1axdvvfVWjrbbtGlTTpw4wYkTJxg8eLCEViEKOB8fn3zbKc/Q0BBra2vdWtqvMwmsQuSD0NBQBgwYwPLlyzl69CgjRozIlSVOMmvKlCk0a9aM//3vf7rXJk2axIQJE1ItpaWUwtXVFRcXlyxds7e3N46OjukOdXjdAitAyZIl8fLy4vPPP+fgwYNotVoGDx5M+/btcXd3z7U9vkuVKsWBAwe4ceMGbm5uudKGECJnXLlyBWtr63xr38bGhn/++Sff2s8pskqAEHnMx8eHAQMG0KFDB3x8fF66c1JsbCy///47p06d4tKlSwQEBHDjxg1CQ0MBMDIywtjYmJo1a2JjY4ONjQ1t27alVq1ambqeX375hf3793P27Fnda3v37uX8+fNs3bo1Vfm9e/eilKJz585ZuOuMNwyArAfWWrVqcfnyZRITE3MtGGZGlSpV2LZtG/3792fYsGGEhYXh6uqa6z+AGBoasm3bNuzt7WnVqlW+DSURQmRMKZWv71H52XZOkh5WIfKIVqvFzc2Nrl274ubmxurVqzMMq8eOHWP48OFYWlqyePFiYmNj6dy5M2vXruXatWtERUURGxvLgwcP8Pb2ZubMmVhbW3PhwgXat29PvXr1WLhwIQ8fPky3jeDgYEaNGsXGjRspWbIkkLRW6gcffIC7uztFixZNdc6CBQuy3LsKGW/JClkPrMWLF6d06dJcv349S9eRG1q3bs2MGTNYvHgxq1evzrPlpypUqMDixYv56KOP8qQ9IUTW6enpER8fn2/tx8fHvxHLWkkPqxB54MGDBwwePJiIiAh8fX2pWLFiumWvXr3KlClTOHfuHOPGjePTTz+lfPny6ZYvUqQIxYsXp0KFCrRv3x5ImhR17NgxNm/eTK1atZg1axbjxo1LEaSUUowYMYIhQ4akGIu6aNEi6tatS8eOHVO1dezYMe7fv59qt6uXSUxMxM/PL0cDK/w7LKBatWpZOi83aLVaqlatiouLC7/88kue9WoMGDCA2bNnc+rUqTdmgXAh3iQ2NjZcuHABR0fHfGn//Pnzebb+a256/SO3EAXc77//jp2dHQ4ODhw5ciTDsLpmzRocHR1xdHTk4sWLTJkyJcOwmh59fX1atWrF2rVrOXr0KHv37qVBgwbcuHFDV2bt2rXcvn2bTz/9VPfatWvXcHd3Z9myZWnWu2DBAmbMmJGp3aied+nSJSwsLDAzM0u3zKsE1oJgw4YNrF27lpiYmDzt8SxcuDCjR49mw4YNedamECLzHB0d8fb2zpe2w8PDuX79epY2OSmopIdViFwSHx/PnDlz2Lx5M5s3b6ZNmzbpllVKMXfuXLZt24a3t3eO9hja2Njw22+/4e7uTrNmzdizZw/FihXDxcWFI0eO6HazUkoxYcIEpk6dmubMdn9/f86dO4enp2eWryGj5aySZTew7t+/P8vXk9PCwgbtTmYAACAASURBVMK4ceMGTk5O/Pjjjzg6OlK7dm2GDBmSJ+23bNmSSZMm5UlbQois6dKlC66urowfPz5LQ6mCCWYDGzjHOUIJxRhj6lKXYQyjNKUzVce2bdto06aN7n3+tZbejgJKdroSItuuXbumGjVqpDp16pSp3ZA+++wz1aBBA/XgwYNcva4ffvhBWVhYqPr166tly5alOObp6amsra1VbGxsmuf27dtXffHFF9lqd+TIkSl2z0pLtWrV1OXLl7NUr6+vb5Z2x8othw8fVk2aNNH9/sKFC8rc3FydOHEiT9qPjIxUhoaG6f7ZCSHyT0JCgqpatao6fvx4psr7KB/VS/VSRZ998NyHoTJURVVR1Uv1Uj7KJ8N6EhMTlbW1tTp06FBO3EaeIIOdrqSHNZtiY2MJDAwkICCAy5cvEx4ezvfff0+fPn0wNTWlcuXK2NjYYG1tTalSpfL7ct8YiYmJHD9+HH9/fwICAggICODWrVu6R91WVlaYmJhgbW2tmzHfpk0bypYtm2fXuH37dsaPH4+LiwuTJk166WD3P//8k5UrV3Lq1CksLCxy9dreeecd1q5di7e3N6NHj9a9HhUVxcSJE1m3bl2aP4kHBgZy6NAhvvvuu2y1e/LkSUaNGpVhmez0sNrY2BAYGJjvKwWEh4en+Hdua2vL+vXr+d///oe3tzcVKlTI1faNjIzQ19cnNjb2zehJEeINoq+vz4wZM/jwww/5888/M5yUuZrVTGUq0USjUKmOR5O0AYAnnuxjH0tYwhjGpF3X6tWYmJjQqlWrHLmP/CZjWLPg4cOHrFq1iubNm2NsbEzfvn3x8PAgKioKMzMznJycsLKyokiRIhw9epRJkyZRqVIl3USMc+fOpdoRR2TO6dOnmThxIuXLl2fixIkEBgZSu3ZtPv74Yw4cOEBQUBB37tzhr7/+YvPmzfTp0wc9PT127tyJtbU17dq147vvviMyMjLXrjEqKoqRI0cye/Zs9u7dy+TJk18aVhMSEhgyZAjffvstVlZWuXZtyZJ3Xqpfvz7u7u661+fPn4+Tk1O6wxbc3NwYP348xYsXz3Kb4eHhXL16lbp166ZbRinF06dPs7xxQrFixShbtixXr17N8nXltq5duzJx4kR69uxJVFRUfl+OECIfjRgxgtKlSzN79ux0yySH1Sii0gyrz1MooohiKlNZzepUx8+ePcu8efPYuHFjvq7xnaPS63pVMiRAJygoSA0ePFgZGxurAQMGKC8vLxUTE5Opc7VarfL391fTp09Xb731lmrYsKE6cuRILl/xm+P27dtqwIABysrKSs2dO1ddunQpy3VERkaqH374QXXt2lVVqFBBbdu2TWm12hy9znPnzqlatWopZ2dnFRoamunzPDw8VPPmzXP0WtITERGhatSoobZv367OnDmjrKysVGxsrLp06ZIyMzNTQUFBaZ53+/ZtZWpqqh4/fpytdg8dOqScnJwyLBMaGqqKFy+erfq7du2qfvrpp2ydm1OOHj2qGjVqlOp1rVarBg4cqPr27Zvjf+eeFx4eroyMjFRcXFyutSGEeDXBwcGqYsWKavHixaneD3yUjzKMMVQMR/EWiuIo6qH49dlggOsoQFHsuc9Pk44ZKSPlq3x1dZ0/f15VqFBBbdmyJa9v8ZWRwZAACawZSExMVG5ubsrMzEzNmDEjS0Ekvfo8PDxUhQoVVN++fTM1tvG/bP369apUqVJq1qxZKjw8PEfqPHr0qKpfv75q3bq1evTo0SvXp9Vq1apVq5SZmZnasGFDlkOJk5OT+vnnn1/5OjJjzJgxauDAgbrft23bVm3ZskW1a9dOffnll+meN2nSJDV58uRst+vq6qo+/PBDpVTS9ysoKEgdPHhQrV69Wrm5uSknJyc1ZcoUZWJiory8vNQ///yjEhISMl3/jBkz1GeffZbt68sJyYExrTGk0dHRytHRUX366ae51v4ff/yhGjdunGv1CyFyxq1bt5Stra2aOHFiih8we6leiggUc5+F00QUvzwLrtefC6zxz49oTfrQKI3qrXorpZQ6ePCgsrCwUFu3bs2vW3wlElizITY2Vjk7OysnJyf1zz//5GjdkZGRasqUKapatWrqypUrOVr3m0Cr1arPPvtMVapUSV28eDHH609ISFDTp09X1tbW6saNG9mu58mTJ6p3796qfv362er5DQsLy7NesT179qi33npLPX36VPfaqlWrVOvWrVWdOnVUfHx8muc9fPhQmZqaqjt37mS77S5duqgJEyao7t27K2NjY2Vubq6aN2+uRowYoaZOnapq1KihnJ2dlZmZmerYsaOqWLGiMjQ0VE5OTmrZsmXq7t27Gda/ceNG1b9//2xfX06pX7++Onz4cJrH7t69q8qXL6927tyZK21//PHHuh8KhBAF25MnT1Tnzp2VtbW1+u2339QD9SDV5CrdRx0UOzIOrCiUgdZAvTP2HVWuXDl14MCB/L7FbJPAmkVxcXGqQ4cOqmfPnioqKirX2lm9erWytLRUf//9d6618TqaP3++qlev3kuDyqtavny5euutt9TDhw+zfO6ff/6p3nrrLTVhwgQVHR2drfYPHz780kflOSE4OFhZWlqmClNHjhxRhQoVUkePHk333Dlz5qiRI0dmq90nT56oCRMmKI1Go1q0aKE2bNiQ7lOFQ4cOqZYtW+p+HxERofbu3asGDx6sTExMVNeuXVVAQECa5546dUrVqVMnW9eYk9zd3VWvXr3SPe7r66vMzc3VmTNndK89UA/UIrVIOStn1VV1Vc7KWS1Si1SwyvzTl+joaFW2bFl1/vz5V7p+IUTe0Wq1ysvLS1WtWlVVXl1ZFY4vnDqG3kdhgCLgucBaDoUViqEoHj5XNgrVek/rHHsamV8ksGbR9OnTVefOnbP0WDK71qxZo2xtbVVkZGSut/U6OHbsmCpTpswr9ehlxZQpU1S3bt0y/Sg/ISFBff7558rCwkLt2rXrldpeu3atGjp06CvV8TJarVb17NlTTZ06NdWxCRMmKH19/XTPDQ0NVWZmZll+wqDVatWaNWuUhYWFevfdd5W5uflLv787duxQPXv2TPNYVFSU+vLLL5W5ubn68MMPVURERIrjkZGRqmjRoun2EueViIgIZW5unuEPoFu3blUVK1ZUvz3+LUeWrVEq6Qfft99+OydvRQiRR2JiYlTzG81Th9U4FG1RjHr2+3AUvs96WO+j+B+KDinPGaQG5fftvDIJrFlw4MABZWVllWfjS7Varerfv78aO3ZsnrRXkMXFxanKlSsrLy+vPGszNjZW2dvbqw0bNry0bFBQkGrdurVq0aKFun379iu3vXbtWjV8+PBXricj3333napbt26qSYIXLlxQZmZmqmjRoume6+bmluVH7QkJCer9999X9erVU/7+/mr79u2qR48eLz3v22+/VcOGDcuwzIMHD1T//v2Vo6Njqn+fVatWTbcHNi99/fXXql69ehn2unf07Kj0ovWURqtJ+xHgc+PSjJSRWqVWpVtXYGBgql5bIcTrpavqmvJffyKKfig6PQuuaX3ce9bjGvbva11V1/y+lVeWUWCVZa1eMHv2bJYuXUrp0pnbReJVaTQaVq9ejYeHBzdv3syTNguqH374gUqVKtGtW7c8a7NIkSIsXLgQNze3DJcc27NnDw0aNKBly5YcOnQoW9ulvkhfX5+4uLhXric9165dY8aMGWzevBkDAwPd60opxo4dy0cffZTu2qUxMTEsXboUFxeXTLeXkJBAnz59uHr1KkePHqV+/fp4e3vTqFGjl56bmTVYLSws2Lp1K+3bt6dp06YEBQXpjhWULVpHjRpFtWrV+OCDD9BqtamOr2Y1R7sfRVtUi9K82rI1oaGh9OvXj3nz5lGvXr0cuwchRN4IDQ3l0KFD3Pn7zr8vKuA94AGwE0hvydbklaqee5sxJWvrWL9uJLA+56+//iI4OJjevXvnabsmJiYMHTo0xbqY/0XLli1jypQped5umzZtKFy4cJpbfMbFxTFlyhTGjBnDDz/8wNy5c3NsgfpatWpx/vz5HKnrRQkJCQwaNAgXF5dUe0hv2bKFiIgI7O3tsbW1TfP8DRs20KBBgwzXTn3Rp59+Snh4OHv27KFkyZJA5rZkhaTAmpkNNjQaDZ9//jkDBw7k3XffJTExESg4gVWj0bB27VouXrzIoEGDUvxA4otv0hqLcVFJ/yFVBEoA9YG9zwrFAX2ASiT9h/QHutDqh5+urqCgIJo3b07Tpk0ZO3ZsntybECL7YmJiOHnyJO7u7gwePBgbGxusrKyYO3cuxa8Vp3DCs2Q6BggAfgEMn6vAG7hMUkB9DEwAWgHGSYcNMaQOKd/r3zQSWJ+zdetWRo4cmS875owePZqtW7f+ZzcWePz4MYGBgXTs2DHP29ZoNPTr1y9VYL1y5QpNmjThypUr+Pv706JFixxtt169evzzzz9ERETkaL2QtNB/0aJFU+0v//TpU6ZPn86qVavw8/PD0dEx1bkJCQm4ubkxc+bMTLd35MgR1q5dy+bNm3U7LcXFxXH27Fns7e1fen5Wd7maNWsWhQsXZv78+UDBCayQ9APogQMHiIqKok2bNly4cAEAV1yTdqlJACoAR4BQ4HOgL3DjWQXNgM3Ac5uzRRONK64opfDy8sLJyYl3332XFStWvDmLggvxhkhMTOT8+fOsW7eO0aNH07BhQ8zMzBg7diwXLlygZcuWbN++nadPn3Ls2DF2dtuJfiF9uAl8A5wh6d9/8WefW4BrQEeSfsitDRgA2/5tU6EYytA8vc88l95YAfUfHMPq4OCQ4Yzp3KTValXp0qXVrVu38qX9/Pbrr7+qNm3a5Fv7Bw8eVE2bNtX9ftOmTcrc3FytWLEiVxd879Kli1qzZk2WznnZzHI/P790/y598MEHauTIkUqr1ao6deqo3377LVWZzZs3qxYtWmTpmpycnJSHh0eK13x9fVXt2rUzdX6/fv2yvG7gnTt3lKmpqXr06JHy9/dXtra2WTo/tyUkJCh3d3dVunRpNfyj4cpAa5D+iNXkpWue/7BCcfjf3xdJLKJavdNKWVtbq3379uX37QkhVNL/3VevXlXbtm1TkydPVs2bN1fFixdXNWrUUAMHDlTLly9XJ06ceOmKQ71UL6VRGY9rz2i8e/I6rK87ZNLVyyUmJqoiRYqkmoGclzp16qQ8PT3zrf38tHz5cjVu3Lh8a//evXuqdOnSKjw8XA0ZMkTVrFlT+fv753q7Bw8eVNbW1ioxMfGlZX2Uz0tnlneP764q9qmYZvg7ffq0srCwUI8ePVL79+9Xtra2qcJ4YmKiql27ttq7d2+m7+HEiROqcuXKqVbVWLlypRoxYkSm6ujQoUOW2kw2bNgwNX/+fBUVFaWKFi1aIHd6evjwoWri2UQ9220x9cfzS9dkEFg1URrV7Ui3AnmPQvxX3Lt3T3l5eak5c+aot99+W5mZmany5curXr16KVdXV3XgwAEVEhKS5Xp9lI8yUkbZCqwv7nT1OssosBbK7x7egkIpRUJCAsWKFcu3ayhRogQxMTH51n5+Ukrly1CMZPr6+sTHx9OwYUOaNGmCn58fxYsXz/V2W7dujampKStXrmTChAnplkveYzqaaBSph41EEw2Al54X+pv1eWrwNMVxrVbL2LFjmT9/PkZGRkyePJmZM2emepy8e/duChcuzNtvv53pe9i0aRPvv/9+qj+/kydP0rJly0zVkdUhAcnGjh2Ls7MzM2fOpHz58vzzzz/UqlUry/XkBqUU0dHRxMfHY9rCNOV4tGTxgDMwBLB+SX2GCpMWJhROdxaGECInhYaG4ufnh6+vr+4zMjISBwcHHBwcGDt2LA4ODlhaWr5yWw44sIQlSePcicr0eUYYsYQl2PPyoVevOwmsz1H5PH40v9vPTxqNhoSEhHxpWynF119/TWhoKCtXrsTZ2TnP2tZoNGzcuBEnJyeaN2+OnZ1dqjLJYTVTb2J6kGiQyFSmAjCGMQCsX78egOHDhzN+/Hhq1arFgAEDUpyqlMLV1RUXF5csjYv09vZm0KBBab4+Y8aMTNWR3cBqZ2fH3bt3CQkJwdbWlosXL75yYFVKERsbS1hYGKGhoYSFhel+/fjxYx48eMD9+/d59OgRISEhhISEEBYWRnh4OFFRUcTExBAXF0dCQoLu+6h2KXhx8QstMAgoAqzM3LWFEPJK9yaESFtMTAxnzpzB19cXHx8ffH19CQoKon79+jg4OPDOO+/g5uZGlSpVcm3cePL7dUadE8k0aDDEkCUs0Z33ppPA+oyenh6mpqbcvXuXcuXK5cs13LlzBwsLi3xpO79ZW1vz008/5Xm7jx49Yvjw4Vy6dAl7e/s8DavJqlWrxsqVK+natSt79uyhfv36umO6meWxUTAWOAA8AaoCrkCnZwUPAuOAW0AjiNoQxdSKU3HAgcqPKzNz5kx+/fVX5s6dy8GDB/Hx8Un1pnvkyBGePHmSpVUy4uPjuXjxYoprhqRJdPfv38fGxiZT9WQ3sOrr62NnZ8fp06extbXl7NmztGrVKlXQfPHXISEhPHz4kCdPnvD06VNCQ0OJiIggKiqK6Oik3upChQrpeo21Wi0JCQlotVqKFi2KkZERxYoVw9jYGBMTE6pXr465uTkWFhZYWlpSrlw5ypcvj6GhIdu3b+cr9RVhhP174c8vXfMr6S9d84I3fdkaIfJCQkICAQEBumDq6+tLQEAANjY2ODg40KpVK6ZNm0atWrUoVChvY9IYxuCAA6648iu/okGje4IGSasBKBSd6YwLLv+JntVkElif0Wg0ODo64uvrS48ePfK8/fj4+EzPqH4TOTg4cOrUKRISEvLsDeLIkSMMHDiQfv360bBhQyIjI/Ok3bT069cPPT09OnTowMaNG3WrJaQ5s/wtkkJOX+A8SbNIewNrSerFmwP0g+iTSTPLS88qTa9evVi1ahXnzp3j2LFjGBsbp7qGBQsWMGPGjCwNzYiLi0NPTw9Dw5TPu318fHBwcMhUXUopQkJC0Gq1XL9+PcOg+eKvQ0JCuHz5Mj179iQ6OprExESWLl2KgYEBhQoVQk8vaSGU5MAZHx9PdHQ0Wq2WEiVKULJkSYyNjalQoQLm5uaUKVOGsmXLYmFhQalSpXSfZmZmlCpViuLFi2fYu/Lo0SNWrlzJ6tWruXbtmm5Zq6IfF0XvbT20Bs8WTUxeuuYAqYcKxIKuYyUOiAEMwFDz5i9bI0ROU0px7do1XTD18fHhzJkzWFlZ4eDggKOjI0OHDqVevXqp3sfyiz327GQnD3nIBjZwnvOEEIIpptShDkMZSmnyZq34gkQC63OaN2/Ob7/9li+B9ciRI9SsWZMSJUrkedsFgampKXXq1OGXX36hV69emT4vmGA2sIFznCOUUIwxpi51GcawdP9BJyQk8Pnnn/PNN9+wbt06OnbsSO3atfN9Hdx33nmHMmXKMGjQIBo3bsz0JdPZW2Fv0mOhYsC85wp3BSoDp0hak88WeOfZsXmAOahLit01dlP0QFGM44xp0qQJhw8fTnNsrp+fH5cuXWLgwIFZuubkdVBv3ryZIlCuW7eOIkWKsHjx4kwFUK1WS40aNShWrBiGhoYUKVIkReBMHmMeFxdHTEwMUVFRREREJO1+8uzpSLVq1bhy5Qp9+/ZNM2w+/1msWLEceax39+5dVq1axY4dO7h27Rrx8fFA0t/ndu3aMWXKFGrWrMms5bP4Xvt90knJS9cYkGLpKr4haTxrzWdlAJKHEl8HVek/sGyNEK/o3r17Kcac+vr6YmRkhKOjIw4ODsybN4+GDRtiYmKS35f6UqUpzTSm5fdlFBiajMZN2tvbKz8/v3SPv2nu3btHrVq1uHr1aqYWMU+WndD0oq5du9KjRw9GjhyZ3ct/7f34448sX76cP//886VlffHFFVf2PltxPYZ/J6slPzLpRCdccMEBB92x27dv4+zsTOHChdm8eTOWlpbs3bsXFxcX/P39C8SallFRUbi5ueGm3IibGUeiQWLqQg9IWnj+DLCapJ645zdDqg18AnSCkl+WxLOpJ61bt9Yd1mq1RERE6ILjuHHjsLa2pk2bNpnu3QwNDSUqKgqlFOXKlcPU1FQXOC9cuEClSpWwtLREo9Gk6uGMjo4mIiJC10saHx+PlZVVqmCZVth8/nVDQ0MaNWrEl19+ib29PaampoSGhurWgs1p165dY82aNfz0009cv35dN+7a1NQUJycnJk2aRNu2bdHT0yMwMJDPPvuMHTt2oNVq0fPUI6ZDDGRjbqEGDb3oxU525vAdCfH6en5SVPLj/aioKN2kqOSQWrZs2ZdXJgoEjUZzSimV5qNmCawvGDZsGJaWlixYsOClZbMbml7k5+dHly5duHHjRoF5JJEfEhISsLW15bPPPqNv377plnvZjPlkLw5K37VrF6NGjWLixIm6R9/R0dE4Ojoya9Ys+vfvnxu3lSlXrlzh+PHjBAQEEBAQwJUrV7jx2Q2ieqcx0SqepLGrVUnqlXsPKA0sfK5MU2AkMBTK/l6WynMrpwibERERGBkZUbJkSYoWLcqtW7do0aIFpUqVokSJEhgaGlK4cOEUPZyJiYmpejjDwsI4ffo0BgYGREZGUrhwYUqVKsW9e/do1KgRlpaWLw2ht2/f5v333+fs2bNZ/r7FxMRgZmbGw4cPMTIyombNmvz000/p7uCVFUopLl26xHfffYenpyc3b95MFVDHjx9P+/btUwxj8fPzY/78+ezfvx+tVkutWrW4fv06g1YMYq3zWqI0mZ8BnMwII45w5D81Xk2I5yVPinp+3GlQUBB2dna6gOrg4JCrk6JE7ssosMqQgBfMnz+fhg0b0rFjxwx3NsrsMkOeeLKPfenO5AsPD2fAgAGsWLHiPx1WIWmSy5YtW+jUqRMODg5Urlw5VZmszJhXPNuLXU1l+/bt3PjoBj///DNNmjTRlZkyZQq1atWiX79+r3z9cXFxREVFERkZmamvDx48wN/fn4sXLxIREYG5ubmuZzAxMZH4YvGpG0lrZnlxeH4+Dzz7/bPRJWVqluG9995LqvNZD2dUVBRPnz7lyZMnHDx4kHLlynH37l0uXLjAkydPMDAweGnvZvLnhg0bKFasGIsXL6Zo0aJcvnyZjh07cvz48Ux93wIDA7M14Qrg2LFj2NjYYGRkBPy741V2AqtWq+Xs2bN8//33eHl5cevWLd2QBxMTE9q3b8/o0aN5++23MTAwSHGuUoqDBw/i6urK6dOnSUxMpEmTJty5cwdzc3N27NhB5cqVubr7Kr+2/RVlmPkVQf5Ly9YIAUmdFxcvXkwx7vTy5ctYW1vj4OBA69atmT59OjY2Nnk+KUrkH/mTfkG5cuVYt24dzs7O/PHHH1StWjVVmWyFpheWGYKkgDN06FBatmyZI4HpTWBvb8/s2bPp1KkTv/32G5UqVdId082YXxkFG0iacDSApF8niwKmAj+Q1BNZD6KORvFnzz/xauNFdU11bt68SUREBCtXruSXX35h6dKl7Ny5M0th88WvUVFJfxeKFSumm0H+/FcDAwM0Gg1KKeLj47l69Sq3bt3C2NhY15v55MkT4uPjMTMzo3Tp0sTqx3Kf+//eW3ozy22B75/7HkQCV5+9DgScCGDRokWUK1eOMmXK6IKmlZUVlpaW7Ny5kx9++IGKFStSqlQpTE1NUwWyjJQtW5ZmzZqxaNEiIGk5q0aNGr30vOShNHuq7uHy4ssMZGCWh9KsWLGC999/X/f7rGzRmpCQgK+vL5s3b2bPnj3cvn0brTZpUpSxsTHt27dnxIgRdOrUSReIX5SYmMjPP/+Mq6sr9+7dIzY2FicnJ8zMzPj9999ZsmQJzs7OaDQali9fTuBXgXRb3o3drXajiiqUJoMnBFoNhnr/rWVrxH9P8qSo53tO/f39KV++vO6R/tChQ6lfvz5FixbN78sV+UiGBKTjm2++4ZNPPsHLyyvFzH1ffGlFq9RhdSBJSwtFkjSRYjowImWR5x/rhYWF0bt3b0qUKMHWrVv/872rCQkJKQLgmjVrWL9+PZ9++ilWVlZERkbi1tiNM2+dQXkq0AP2AdGkDKwDSZpR7w6UImmMZ0MgEfR+0cN0hCmGhoZEREQQGxtL3bp1MTExSTdovuwrJI2jCg0NJTg4mHv37nH37l3u3buX4tdxcXGULVuW4sWLc+3aNUxMTGjcuDGlS5emUKFCuklFUVFRBAUFcePGDe4MuEP8rHhIzkqjn93PAZJ6VZM9BKoB64AuwFySVhM4CYUTCmPvZQ9L4Ny5c5QtWxY7Ozvd544dOzA3N9eFzezq0aMHrVq14sMPP2Ts2LFUr16dDz/8MM2yOTWU5syZM7z99tsphtJ4eHiwY8cOduzYkap8bGwsJ06cYOvWrezbt4+goCBdQC1RogROTk4MHz6cTp06UbJkyQzvNzY2lk2bNuHm5oZSisjISKytrenatSsrVqygZcuWfPHFF5ibmwPw/fffM2fOHDZu3EifPn345tQ3bK24Nd1la+IT4intUxqvJl7SsyreKM9PivLx8cHPz49ixYqlGHfasGHDNFcyEW8+GcOaTZ6enowcOZJp06YxceJEDAwM6E1vPPFMPQzgb5JCgwFwCWgF7CEpLD2TPHHi/f3vM2HCBNq0aYO7u3u+7vCUGckLqWe39zGt3sgXX0veZez5MBgTE8PNmzexsrLCpqUN+9fsJ7HIcxOQZgN3+DewXgIcn72WRt4oSlHWHVjHvHHzsLa2ZtOmTWkGE6UU4eHhKQLn81/v3r3LnTt3uH//PomJiZiamlKyZEldT2ryn2d8fDwxMTFERkby9OlTnj59ilKK0qVL89Zbb+kerxsbGxMREcGdO3cIDAwkMjISJycn7N62w22sW9I93wQqkfT36/nnIskzyw8A40kq1+jZ96QSGCgDbmtuU5rSLvtdFgAAIABJREFUJCYmEhgYiL+/P/7+/pw8eZLjx49jYWFBw4YNsbOzo0GDBtjZ2VGpUqUsjQO7fPkyzZo14/DhwwwZMoSVK1fi5OSUqlx2xx+/KHm3GRcXlxSbFpw/f56+ffsSEBBAZGQkR48eZfv27Rw4cIC7d++ilEKj0VC8eHEaN27MoEGD6Ny5M2ZmZpm6z7CwMNasWcPSpUuxtLTk6dOnlCpViunTp7Nz5058fHz4+uuvad++ve4cT09PxowZw4EDBxg5ciT9+/fX7WqW3rI1faP6Yl/RnpMnT6b5lEeI10HypKjne0+joqJ0PafJnzIpSiSTwPoK/vnnHyZPnsylS5eYtGASU/pMIVYTm/FJl0kKrMtJWivzOXpxelRoWoHls5fTvXv3HBkcnpiYSHR0dI4FyrQCZqFChbLdC5mZr0WKFEnze/Hw4UPmzJnDZsvNxLjEZBxYNwKLgXbAJsCSpCWe/vfsex+jh8kyE+abzKdGjRrcvn2b69evc/36dYKCgrh//z4PHz4kJCQEpZTuuvT19XU9oNHR0cTExFC8eHHdo/vMzGy/ceMGAwcOxMPDgzZt2nDnzh327t3Lr7/+yuHDh7G2tqZTp0507tyZatWq4erqyrJly4j3iIceZGtmOYlgsNeAxdcXM2rUqFSP+WfNmsWTJ0/46KOPOH36tC7I+vv7ExkZmaIntkGDBtSsWTPDH642bNjAokWLuHnzJk+ePEn1+C5LO3Y9kzx+8/nQmpCQwNChQ9HT02Pjxo2610NDQ9m3bx8DBgygdOnSBAcH6wKqkZERjRs3ZsCAAXTp0iXL/0EGBwezYsUKvv76a+rVq8fjx491y6M9fvwYFxcXhg0bxty5c1MMHzh06BD9+/dn7969HDx4UPc1eTJbRlxcXP7P3nmHRXWte3hNgSnUaQwdpSMgIIqCYsMG9l6wV6xoNGpsscSuMfYES4w9mliuNZrEhJjEFjWxoMbYe+/0ee8fHPcRwZZyknvuvPPMo7P32muv2XuG+a1vfUVkZmaKDz744I3GasXK30FmZmaRSlFXrlyRgqKeitSSJUtag6KsvBCrYP0T2L59u0i9nCpOtTlVfE1wIQoqES0RBcvUUUKIdFF46VYIocxRipRrKaLVxVZ/mnUyOztbaLXaQiLwzxSUWq22WMd24Hc9f8+xHRUdxU7zzsIDeF6wThBCDBcFS+LDhBA/ioIl8v1CiKcFl5YKYdPVRlgsFmGxWIRKpRL29vbCyclJShz/1NfzWeH57P+dnJzeyCp+7949ERUVJbp06SIePXoktm7dKq5cuSJq164tEhMTRe3atYXJZBJ3794VEydOFLNmzZISzutr68Xtz2//2y3gDVBmK4VdXTvhe8dX3L17V4wePVq0bdtWKBQKcf/+feHn5yf2799fbHDbjRs3JPH6VMxevXpVhIWFFbLEhoWFScIUEB07dhSffvqpOH78uPD19ZX62y/2i4pzKorcJbnF+x4/ZawouH87RcHEQxR2pXny5Ilo3bq15Dayd+9e8dlnn4n09HRx69YtaRwqlUpUqFBBtGjRQtStW1f4+Pi8+QUUQpw9e1ZMnz5drFy5UiQkJIjr16+Ly5cvizFjxoiyZcuKnj17iocPH4oFCxYUKau7b98+UbduXcntomrVqmL//v2F/LJfxqVLl0RERIQ4e/bsK10UrFj5T/I0KOpZy+mJEydESEhIIeupNSjKyptiFax/Em1FW7FCrHh5o3xRIJS+EUIMEcWWXJSvkAtVN5WQy+VCJpNJ/77OUwhRaHb6NJBHiFeLwNdp87p9PMvrjr249/G6z7uf3BXZtZ6zbD8vWGf865o/Ef9eNq8vCoRPasFL9U618OnrI+zs7KScoWq1+rWfKpXqtdvevn1b7Ny5U4wbN05cuXJFhIeHi6SkJJGUlFSoCtTNmzfF5MmTxdy5cyWhqtVqRXBwsJDJZKLq6qpiqnlqQfGA1+WJEGVXlRUfBH8gunbtWhDElZ0tHj58KN577z1x8uRJcezYMbF8+fLX7vLBgwfi8OHD4tChQ+Knn34SBw8eFKdPnxaBgYGSgD158qT45ZdfxG+//SamTJkikpOThVwuF01EE7F+3foX+x4LURAo1kgUFEJYKiTB+tSVpuOmjqJPnz4iPz9fPHnyRNy/f18qHKBUKkXZsmVF8+bNxbZt20SHDh1E69at3+CCFeaXX34RkydPFtu3bxdNmzYVV65cEYcOHRIjR44U7dq1EzNnzhTvv/++GD58uOjbt2+RH+Vjx46JhIQEsWDBAlGnTh1RoUIFkZKS8sZ5llu1aiViY2NFamrq734vVqz8EQDx22+/FakU5eXlVcjvNCIiwhoUZeUPY01r9SdxX9x/dSOFEKKSEGK5KEjk3q9oE7cQN5HcN1l4enoKDw8P4enpKZV8/DNF3n+ij/8UrzVZKF3MtueGWCumlhj76ViRlZX1Ws8HDx6IGzduvFbbp8nwHz9+LLKzswsJe51OJ27cuCFWr14tNmzYINRqtZDL5eLq1atS8M/TyYevr6+4efOmkMlkombNmuKD8A8KUlm9Lwp8WF9i3JUhE2qhFiErQsTPvX8WLsdcxKFDh6TKXm3bthVjxowRx48fF3PnzpWWzF/EgwcPxObNm8XBgwelHLHPpnsSoiCZ/oMHD8SPP/4oLl68KPLy8oTJZBKpqali+PDhov2g9mJr360F5WOFEOKAKJhoPE9vIcRkUbBS8QwIxLrMdWJd53VC3BJSBayYmBjRpEkTkZiYKEJDQ6X3cefOHXH8+PEXX6QXAIjdu3eLSZMmiUOHDon27duL2rVriw0bNoi3335brFmzRhw5ckTExsYKDw8PceDAgWKtpWfPnhV16tQR06dPF/Xr1xdjxowRLi4uomvXrkVP+gr69+8vkpOTRZ8+ff7xvu5W/ju4evVqIcvp/v37hb29vSRMx44daw2KsvK3YBWsb4CTeIMvaJ4osBgVw9XjV8Unn3wibG1tRW5urrh7967QarWiZMmSokSJEsU+/7+WbH1KaVFafG75XGTJswqubZ4osGbni4Ja60ohRGUhhLcQYqIQ4h0hxF4hxC4hxJR/dZIpxM2vbwploFLExMT8KYL7+vXrYvv27WLr1q1i586dwsfHRyQlJYnExERRvnx5MWrUKHHnzh0xbtw4SdieP39eLFiwQGzatElavs7OzhZubm4iPDxcpKeni8aNGwtfX18xZ84c8eTJE2H83CjsbtmJC8kXBIkU5GN91k3giRBCJoTNVzbCZoaNyPgxQ+Tm5hYqdyqXy8X8+fOFxWIRSqVSpKamiqFDh4pSpUoJV1fXQlbkq1evioyMDHH27Fnh6+sr/P39RcmSJUXVqlWFl5eXsLe3l9rm5uaKK1euiIsXL4rx48cLs9ksZDKZqFixonjw4IGYemOqyMnMeblbw1pRIMaTXrAfIdyGuYlej3uJpKQkERER8UIBFxoaKlavXv3a99BisYgtW7aISZMmievXr4sePXoIb29vsXDhQtG7d28xf/58IZfLxdChQ8XatWvF+++/L1q1alXs5+fq1auiZs2aYujQoSI5OVn89NNPYu7cub+7itrTTBKbN2/+W0pGW/nv5t69e0UqRWVlZUmW0759+4py5coJs9n8dw/VihWrYH0TSovS4nPxeaE0PEIIIW4IIb4WBfXdNaIgYnvVv57P80QIr/teonOvzkKr1YqMjAxx+PBhkZGRIW7duiVsbW3FkydPxIkTJ0RWVpa4cuWKOH/+vNBqtS8UsyVKlCi2Pvx/A/n5+WLnzp3im9XfiKwPs4RQCyHeEwVlR5+yXBT4PY4WQmwUBenEJomC0qVLhRDB/2onE2JPyh4RfjNcaLVakZCQILp27Spq1Kjx2mnF8vPzxf79+8XWrVvF1q1bxW+//SYSEhJEUlKSmDFjhnB3dy/UfufOnWLmzJnCxcVFnDt3TvKHtLGxEQ4ODuL+/ftCoVCIWbNmiV27donTp0+Lw4cPC19fX1G9enVx7949Ub58eXHz5k3ROri1OLP2jNjy9hah6q4S0Z2ixY79O0SEd4R4sveJiP8tXrjIXMT4r8YLIYTYvXu3SExMFA0bNhTTp08XOTk54u7du6JSpUoCEO3btxc2NjZixYoVIjs7WzRu3FhkZ2eLVatWiYcPH4ry5cuLxMREIZPJRHZ2tsjKyhI///yz2Lt3b7EW5sePH4vMzEyhUqnEkydPxObNmwsue3fZy8XqQ1Hgc7zzJW20QtQYUEOMECNeeY9eNxdrbm6uWL16tZg8ebKwtbUVffr0ESdPnhSTJk0SHTt2FBkZGZJY7NWrl0hISBBHjx59YUaBu3fvitq1a4uOHTuK3r17i+zsbNGhQwcxY8YM4eHh8crxvIj+/fuLmTNnWgWrlT/E06CoZ62nT4OiYmJiRKtWrcT06dOtQVFW/rm8zF8xOjoaK//mOtdRo0Y8/7ghEJUFwkkgHAQiTCDSirQqeGQK5GY5crkcGxsbqlevzrJly7h58yZHjhxh2bJlDBo0iBo1amA0GjEajSQkJNCzZ09Gjx7N1KlTmTBhAj179iQxMZGQkBA0Gg1Go5GyZcvSrFkzBg0axJw5c9i8eTNHjx7l4cOHf/ele2MuX77MuHHj8PHxITo6mrS0NOrl1EOWLyv+ur7iIcuX0djSmP3799O+fXu0Wi02NjbI5XKUSiXR0dHMnDmT8+fPFxnLjRs3WLZsGW3atMFgMBAeHs6QIUP45ptvyMnJeeF7yMzMRKPRcOTIETp16oSjoyPu7u6YzWbs7OzQaDS0a9eOb7/9loCAAFJSUsjMzASgQ4cOyOVyfH198fLyYsuWLbi4uDBmzBiaN2+OWq3m/v372NraYjAYOH36NMePH8fV1VUaU3Z2NlqtlgoVKtCvXz8sFguffPIJ1apV49SpU1SuXJny5cvz008/MX36dOzs7FCpVIwePZrc3Nw3vmfr1q0jKSmp0LYTJ07gssel8N0YLhAdnnn9lkCMeea1j0DsLHoP61HvtcaRnZ2NWq2WruXzPH78mFmzZuHt7U316tXZuHEj7733HkajkW7dunHhwgUArl69SvPmzfH39+err7566TkfPXpEbGwsAwYMwGKxADBkyBAaN24svf695OTk4OHhweHDh/9QP1b+/5Cbm8vhw4dZsGAB3bt3JzIyEo1GQ3R0NCkpKSxatIgjR46Ql5f3dw/VipVCCCEO8KIYmhftwCpYi6UxjZHx+0VTE5rwyy+/kJycjFqtxsbGBrVaja2tLdWqVWPBggXcuHEDAIvFwuXLl9myZQsTJkygRYsWBAUFodFoiIqKolOnTsycOZNdu3Zx8uRJ9uzZw+rVq5k0aRIpKSnUqVOH4OBg1Go1RqORcuXK0bx5c95++23mzp3Lli1bOHbsGI8ePfqbr2oBeXl5bN26lUaNGuHs7Ez37t05cOCAtL/R+EaIR7/nygvEY0HiyETu3LkDFPxB/+KLL2jWrBkqlQp7e3sUCgU2Nja4u7vTsmVLOnXqRLly5XB0dKRRo0akpaVJYuZ12Lx5Mw4ODjg7O+Pv74+Liwuenp44OjoSEBDADz/8QFpaGkajkZUrV0rHjRo1CoVCgaOjI23btqVLly7MmzePBg0akJSUxKRJkyhVqhR5eXkIIXj33XelY+Pj4/n888+l16GhoaSnpxMZGcnIkSMJCQlh586dAOTn5zN//nz0ej0xMTGUKlWKgQMHYjAYSElJ4fLly290/wYPHsyYMWOk17m5uezYsQOfdJ+XC9YIgTAIhPlfT7lA6ARiUuF72I52rz2WUqVKFRF4t2/fZsyYMbi4uNCkSRO+++47Zs6ciaurK61bt+bUqVNAwfduwYIFmEwm3nnnHZ48efLSc2VlZVGzZk06deokidPvv/8es9nM9evXX3vML2PChAl06tTpT+nLyn8XFouFX3/9lRUrVtC/f38qVqyInZ0dwcHBtG/fntmzZ7Nnz54XTuCsWPknYRWsfyL72IcW7e8TTY8EZbqX4cGDB0CBpefjjz8mPDwcjUaDjY0NBoMBjUZDpUqVmDVrFhcvXiwyhkePHrFnzx4+/PBDUlJSiI2Nxc7ODh8fHxo2bMi7777LunXrOHPmDBaLBYvFwtWrV/nxxx9ZtWoVEydOpEePHtSuXZugoCDUajUmk0kStIMHD2bevHls3bqV48eP8/jx47/0mj5rTS1btixpaWnSNQK4c+cOgYGBKJVKwuaEvblofSRQ9Vfh5eWFu7s727dvL3T+x48fk5aWRlhYGHK5HJlMhhACGxsbbG1tqVevHkuXLpUmEq/i559/pnnz5jg5OaFSqTAYDJQpUwYHBwfs7e15//33uXv3LsnJyYSFhZGRkSEdu3jxYpRKJfb29kybNg0PDw9u3bqFn58fO3bswMHBgY8++oiWLVuyfft2hBCFLOjLli2jVq1a0uuIiAgOHTrE9evX8fDwwNPTs4jFLyUlBb1eT0hICPv27ePmzZsMHDgQnU7H4MGDuXXr1mu97ypVqjB//nz69OmDv7+/dB3FoIIJg8gViEyBGCoQbf/1/1yBuCUQV595egrEGoF4+O97qEHDFKa81jgAmjdvzooVKwC4ePEiAwYMQKfT0blzZ44cOcKiRYvw9vamXr16hYTtiRMnqFKlCuXKlePnn39+5Xny8vJo1qwZTZo0kazSjx49IiAgoNDE4Y9y69YtnJ2d/zQBbOX/LpcvX2bDhg0MHz6cmjVrotPp8Pb2pmnTpkyaNImvv/6a+/fv/93DtGLld2EVrH8y85j3xqJVna+m/8n+2Nvb4+joyDfffFOoz6NHj9K7d28cHBwwmUzY2tri4+ODg4MDMTExTJ48mV9//fWFY8rPz+fUqVOsWbOGYcOGUbduXTw8PHB0dCQ+Pp6+ffuycOFCDhw4UGSmnZ+fLwnalStXMmHCBLp3706tWrUIDAxEpVLh4uJCTEwMLVq0YPDgwcyfP59t27aRkZHxSgtUceTl5bFlyxYaNmyIs7MzPXr04KeffirS7ssvv0SlUmFra4u3tzc+Pj4oeivQ5Gte6R4gQ4bWoiV8bjghISHodDqUSiU6nY6uXbuSnp7OuHHjiIuLw8HBgfr16zNlyhTGjh1LTEwMjo6O+Pr6olar0el0qFQqIiIiGDt2LD/99FMR4XfgwAEaNmyIi4sLFSpUwNHREY1Gg729PTqdjkaNGnHx4kWOHDlCcHAwnTt3LjQZ+OKLL7CxsUGn0zF69GgCAwPZsGEDa9euJS4ujrVr11KrVi3efvttRo8ejb+/P0qlkqysLKmPzMxMjEYjv/32G1BgYT18+DAWi4XSpUtjMplYsGCB1H779u14enpy48YNVqxYgdlsZuDAgTx+/JiLFy/SvXt3DAYD48aNK9a15MKFCwwfPpyIiIgCcSrEv4WqEDg5OVG9VXVkWTLEu0LaLj3fLebO+YgiLgFq1Nzg9SYMAKNHj6Z79+506tQJnU7HW2+9xfnz51mzZg1BQUFUqVKF77//XmqfnZ3NuHHjMBgMfPDBB6+1VGqxWOjSpQsJCQmF7kHfvn1JTk5+7bG+Lt26dWPs2LF/er9W/rncvXuXnTt3Mn78eBo1aoSHhwcGg4E6deowatQoNm3axLVr1/7uYVqx8qdhFax/AU9F66vcA2QWGeKxwHGwI7dv3+bevXvExsaiVCrp169fEV/BJ0+esGzZMuLi4nB0dMTDwwN7e3tCQ0PR6/WULl2a0aNHc+TIkdfyjbt58yZffvkl06ZNo127doSHh6NWqwkNDSU5OZkpU6awY8eOl1pu8vPzuXLlCj/88AMrV65k/PjxdOvWjZo1axIQEIBKpcJsNlO+fHlatmzJkCFDJEF74sSJQoL20qVLjB07Fm9vb8qWLcuCBQte6GM7YMAAhBCoVCo+/PBDevbsiUKhYN++fexnP6pNKuTZclR5qsJX/UmBwGlCE/azn+zsbOrWrUvjxo2pWrUqMpkMmUyGUqmkWbNm7Nixo9jlstOnTzN27FgCAwNxc3MjKioKJycnDAYDer0eo9FI586dmTBhAjVr1sTNzY0aNWrg7OxMgwYNcHd3RyaT4eHhwZYtWwBYsmQJRqORJUuWFDrXwYMHJV/kRo0aMWTIEFq0aIHFYqFcuXKsX7+e5ORk5s2bR506dWjTpg2NGjVCq9UWuX4DBgzgnXfeISsrC61Wy6NHj9i5cychISGcOHECd3d3Pv30U7KysnB3d2fXrl3SsTdu3KBNmzb4+fnx9ddfA3Dq1Clat26N2WxmwoQJTJw4kbi4OLRaLUIIlEqlJFJlMhmOjo706dOHr7/+mnr16lGiRAkifotA5L2hZfyZiUcTmrz8g/4Me/bsISYmBpVKxdixY7l16xZbt24lKiqKsmXLsmPHjkLfnR9++IHQ0FCSkpI4d+7ca53DYrEwaNAgypcvX+j6f/XVV3h4eEiuJ38mR48exdXVtZA4tvLfw5MnT/j+++/54IMPSE5OJiAgAHt7eypXrszAgQP59NNPpVUzK1b+W7EK1r+I/eynCU2QZcmQZRYWroosBcpcJU1owpKjS9BoNJQvX57s7GwsFgsTJkzAxsaGwMBAzpw5U2z/x48f56233kKv1+Pn54erqyvu7u5UqFABd3d3AgICGDJkCPv27XujP2JZWVkcPHiQxYsXk5qaSpUqVXBycsLNzY3ExESGDh3K6tWrycjIeC1LU35+PpcvX+b7779nxYoVkqCtUaMG/v7+2Nra4uzsjE6nw9bWlqioKIYNG8b27ds5ceJEEbH46NEjSpcujRCCmJgY7t69y/z585HL5SxbtgyA9PR0ZDIZBy8eJPlQMmKpQPulloTLCYhBgguZF7BYLBw+fJgJEyZQsWJFFAoFnp6edOnSBY1Gg52dHSaTidTU1Je6PVgsFvbv30///v0xm80EBwdTrVo1jEYjSqUShUKBQqFAqVQSHh5OZGQkbm5u0jXduXMnjx8/plOnTgQHB3PkyJFC/Z85cwYnJyd0Oh1BQUGkp6fj4uLCtWvX2LVrF4GBgWRlZaHT6bh06RKurq44Oztz7tw5nJycioijp8FX33//PaVLlwagevXqkkj++eefcXFxoX///tSuXbvY97xp0yY8PT3p0KEDM2fOpEaNGtjZ2UmW0afBagqFQvLF9vX1Ze/evVy7do2ePXtiNBqZNm0au3btwraiLbInv8/3W4uW/ex/6WfQYrHwxRdfUK1aNXx8fBg2bBi+vr6kp6dTqVIlSpUqxbp16wp9T+7fv0/v3r1xdXVl9erVb/QdmjBhAmFhYdy+fbtQfz4+PmzduvW1+3lTatasydKlS/+y/q38Z3gaFJWWlka3bt2IjIxEq9USHR1Nz549Wbx4sTUoysr/S6yC9S/EYrGg8lQRvSoap41OBJ0MQrlCSZtDbQiOD5Z+BOfPn4+DgwPt27eXtv30008YjUbUajULFy584Q9mZmYmK1eupGrVquj1esqUKYNOpyM8PJxatWrh5+eHl5cXqampfPvtt7/rj5zFYuHcuXNs2LCBMWPG0LhxY0qWLImdnR3ly5ene/fuzJs3j++///61sw5cunSJMWPG4OXlRUREBEOHDmXhwoW89957dO3alRo1auDn54etrS1ubm7ExsZSs2ZNKXq/du3aZGRksG3bNhQKBYMGDZL6jouLIzQ0FIBx48Yhl8spWbIk169fRyaTUbt2bdzd3fHz86Nv375s27aNGzduEBsbS2pqKsePH8fd3R21Wk3NmjUJDAzkxx9/fOV7ysnJYeLEiZjNZoQQ0v1zcHCQxi2TyVCr1bRt25bWrVvTsmVLwsLCaNu2bZFrd+PGDdzd3bGzs8NgMHDkyBEiIiKYs2YOk5mM+1fulD5XmoSrCXjM9ODU3VMolUree+89AIxGY7G+tZUrV6Zu3boMHjyYPXv24O3tXSijwY8//ohSqWTatGmFjrt37x6rVq2iQYMG6HQ6SaAqlUrJLUOtVqNQKJDJZISFhbFmzRrat2/PzJkzmTBhAgaDgdTUVG7dukVaWhpyuZyGDRvS93jfAl/WNxSr85j3wvuRl5fH6tWriYqKIjQ0lGXLlpGTk8PevXuRy+V4e3vzySefFPlObNiwQZq8PCs6X4f58+fj6+tbJCita9eudO3a9Y36elM2b95MmTJlrFa2/0NYLBZOnTolBUXFxcVhZ2dHSEiIFBS1d+9ea1CUFStYBetfyq+//oparWbSpEmULVuW1atX4+rqypo1ayhRooTkl2mxWGjTpg06nY7JkydLxz948IA6deqgUqlISkri7t27Lz3fiRMnGDRoECaTiYiICOLi4rC3t6dq1aq0aNGCiIgIXFxc6N69O9u3byc7O/sPvb979+6Rnp7O7Nmz6dKlC9HR0Wg0GgICAmjWrBnvvfcemzdv5uLFi1gsFvLy8ti8ebMkeFJSUjh48OBLz5GXl8fFixfp1q0bQgjs7e0JCgoiISEBLy8vyS0gNjaWNm3akJqaikwmY+TIkZw8eZJ69epha2srRfurVCpiYmKkqO9nuXv3LpGRkYwYMYJ79+5RpkwZ1Go1s2fPxmw2S0vpz2OxWNiyZQsVKlQgODiYXr164evrS1hYGL6+vgghcHBwQK1WExAQgJ2dHeHh4ZhMJoQQhIaG8uGHHxYKonv06BGhoaGo1WrMZjMbNmyg5+KeuOx2QW1Ro8ov7OqgzFGizFUi3yBnd/ZuAFxdXYuN5l+wYAE2NjZcuHCBhg0bMnv27EL7b9++jZ2dHXq9nilTptCqVSvMZrPkLmFra4uNjQ2RkZFEREQgl8tRqVQ4ODgQHBzM9OnTWbVqFWXKlKFMmTIYDAbMZjNNmjSRrnvPnj2RyWSMGjWK8+fPYzabiUqLQpGleHV6snyBTbbNC8VqZmYmH374IX5+fsTFxbFp0yby8/PJyMigefPgwV2zAAAgAElEQVTmuLm54e7uzt69ewsdd+XKFZo2bUpAQEAhV4jXZeXKlXh4eHD69OlC27ds2YKPj89fHuySn59PQEAA33333V96Hiu/n6dBUcOGDSsUFNWsWTMmT55sDYqyYuUlWAXrX8iKFSuws7Nj2bJlxMfHs3//fkn4jBo1in79+kltHz9+TEhICE5OTqxfv17abrFYmDNnDmq1GoPBwLfffvvK82ZlZbF69WqqV6+O0WikTp06xMbGotPpaN68OSkpKVSoUAGdTke7du1Yv3797wqOKo7c3FyOHj3KihUrePvtt6lZsyZ6vR6NRoNKpcLV1ZUuXbrw448/vjRP6VOys7OpXLkycrmcNm3aEB4ezpMnT7hz5w56vZ6AgAB+++030tPTWbp0KWXKlEGhUODu7o5CoShkBWzZsiUBAQGYTCZ27tzJr7/+WkSA3rhxg5CQECZNmkReXh5RUVHY2dmxc+dOGjZsSHh4uCSy8/PzWb9+PdHR0YSFhTF06FAiIiIoV64cU6dOJTQ0lFKlSqHX66lUqRLlypXDZDLRrl07aZlPoVBgMBiIiYnB2dmZiIgIhgwZQnR0tORPPGLECEZdGYV4/C+/51eIuaeWRy8vr2L9Lnv16oVKpWLr1q2YzWbp3t+5c4eNGzdSq1YtbG1tJd/Tpy4NUVFR9OzZk7Zt2+Ll5YXZbMbT0xO9Xk/ZsmXR6/UsW7ZMsvB99dVX+Pj4IISgdOnSfP/99+Tk5FCpUiWUSiWfffYZmZmZlC1bloEDB2I0Gvni9hdUvV0V8UQUEeUaNKhRk/g4EecazkUC8e7du8ekSZNwc3Ojbt26knA7d+4cnTt3xmg0MmnSJB4/fkzLli0lF5L8/Hw++ugjTCYTw4cP/13WrKe5cJ936bh9+zYeHh6Sz+9fzZw5c2jatOl/5FxWXs6dO3fYsWOHFBTl7u6O0WgkMTGRUaNGsXnzZmtQlBUrb4BVsP6FpKSkYGNjw+bNm6lVqxZ37txBrVZTq1YtTp8+jclkKmTl/PXXX9HpdDg5ORX5Mf7ll1/w9PREo9EwaNCg1xJ7T/scMmQIZrNZskKGhYXh5eVF7969GT58ONWqVcPR0ZFmzZqxcuXKP2WGn5eXx6ZNm6hfvz46nY4OHTowZ84cJk6cSMuWLQkODkaj0RAZGUnHjh2ZMWMGu3btKuRzefToUel6HDp0CH9/f9LT08nOziYoKAhnZ2du3brF8ePHmT59OtWrV0cIgZeXF9OnTycjI4OSJUsSEhKCXq/nk08+oWHDhshkMqpUqULJkiWxtbXFw8ODihUrkpyczPDhw5k6dSpubm68++673L59GwcHB3Q6HStWrGDp0qWYTCZatGhBWFgYZcqUYeLEicTHxxMcHMz8+fNp3LgxHh4eks/qnj17pPf05Zdf4urqioODAyVKlKBfv37o9XpiY2NxdHQkMjISV1dXKfDLzc2N9j+2R/5E/sbL5YbhhiLWvo0bN+Lj40OvXr0ICgqibdu29OzZsyDDwr+E6VNLanBwMElJSeh0Onr06IGfnx9ubm6EhIRgb29P06ZN2bRpk/RZ3L9/P6VLlyY+Pp4aNWpQokQJhg0bRqVKlVi0aBGenp6o1Wo0Gg2//PILUBDd3rRpU6pVq8asWbMAyMjIQBgFk/MnE7I/hJBfQ2hHO6YwRcoGsHz5ckJDQ8nMzOTq1asMHToUvV5PcnKylHLq2rVr0vUdMWJEoRWKsWPHMnToUDIyMoiPj6d8+fLSmN6U9PR0TCZTsW4jbdq0KTQx/at5+PAher2es2fP/sfOaeXfQVEzZsygTZs2BAQE4ODgQOXKlRk0aJA1KMqKlT8Bq2D9CwkLCyMgIIB169bRsGFDAHQ6HXq9HovFQqVKldiwYUOhYzZs2IDRaMTNzY1Lly4V2vfw4UOaN2+OnZ0doaGhnDx58rXHkp2dzdq1a6lZsyZGo5F27drRuXNnPDw8iIqKYsyYMUybNo2kpCQcHByoW7cuixcvfu08m0+5cOECo0ePxtPTk5iYGBYtWvTC4gOPHz9m7969fPTRR/Ts2VNyYfD29iY8PByZTEZgYCDHjh1j3bp1lCtXjvz8fBISElAqlbRq1YoSJUrg5eVFjx49SE1NRS6Xc/PmTekcdnZ21K5dG6PRKI1PqVSye3fBsnlubi7nz5/n22+/ZcmSJYwePZqOHTtSvnx5KWDKwcEBo9GInZ0d/v7+0v/d3d2pXLkyXl5ezJs3TxJNVatWlVIgPZvpYe3atZhMJubOnUt+fr4UrKXX61EqlXTt2pW6detiY2Mj+YXG9ostPiDptkA0EgitQHgLxIqiolX2RMbas2ul82/YsAFHR0eaNm2Ku7s7QgjJkvrUp7Nbt26kpqbStGlTRo0aRVBQkJQzVqfTERMTw7x584r17bxx4wYpKSlotVrs7Oz44IMPGDNmDIMGDeLAgQNoNBr0ej0uLi60bt2a8ePHExwczMcff0zp0qWla7V06VKUSiUAw4cPLzZdk8VioVatWkRERKDT6ejTp48k0u7evcvw4cPR6/X079+/2CwXq1evJjAwEIPBwOzZs393AMvBgwcli/3zfPbZZwQEBPzluYqfZ+DAgYV8uq38ueTm5nLo0CEpKCoiIgKNRkPZsmWloKijR49ag6KsWPmTsQrW38Hp06f56KOPpEjqEiVKYDAYpOVns9lMTEwMcrmciIgIhg8fTvPmzQGIiYnBYDBw7tw5FixYQJMmRVPyDBkyhICAACIjI4sNYlq8eDF2dnbY29uTlpb2xrP206dP88477+Dq6kqlSpUYOnQobdu2xdnZmVq1avHhhx+yaNEimjZtiqOjI9WrV2fu3LkvrG6Um5vL//zP/1CvXj10Oh29evXi0KFDbzSmp+Tk5EjppeLj46lXrx6enp4olUoMBgP29vYIIfDz82P8+PEcPXpUev8BAQFUrFhR6uvBgwfY2trSoUMHdDqdtN3e3p6BAwe+ciwZGRm4ubkxcuRI7O3tMZlMqNVqnJ2dMRgM0pK5RqPB1tYWs9mMk5OT5K985swZcnJyyMrKom/fvpQsWbJQda5nr9+0adNQKpWSv6udnR2zZ8/G+4B38SmfWglEC1GQQP87gXAUiKPPtckThJ8Kp1+/fri5uSGEQK1WI5fLpWj+ChUqsH79eh48eMDJkycZO3Ysbm5uaDQaqlatSlBQEO7u7lStWhU/P79Ck4GnPHnyhEmTJmEwGOjbty83b97k+PHjxMbGotfradu2LQqFgoSEBPLy8njw4AEpKSnIZDIaN26Mm5sb6enpUn8pKSloNBqgIGhu2LBhhc536NAhWrVqhV6vx87Ojv/5n/8BCvx+J06ciNFopEuXLsWW0gXYvXs3fn5+aLXaN6pO9jwnT57Ezc2t2CIA169fx2w2F8rn+p/i7NmzGAyG/5Nll/9pPBsUlZqaWiQoas6cOezdu9eaTsyKlf8AVsH6mty+fZv333+fcuXK4eLiQocOHZgyZQqbNm3i1KlT3Lx5k3v37nHnzh0uXbrEggULsLOzo1KlSpQoUQK1Wk3v3r2pU6cOERERfP7559y7dw8nJ6ciVszc3FyqVq1KREQEDRs2LHamfuzYMfz8/NDpdNSvX/+NLaFQIA4///xzateujcFgoFevXkydOpW6devi5OREcnIy69evZ+3atbRt2xadTkdcXBzTpk3jzJkzXLhwgXfffRdPT0/Kly/P4sWL/1Ap17Nnz+Lq6oqdnR1ffvklW7dupW/fvvj5+aFQKPDz80MmkxEREUHp0qVRq9WUKlWKNm3aMGDAAORyeSHxcPToUYxGI4MGDcLBwUHaHhcXR3h4+CvHk5WVxfDhw5HJZCgUChYsWEDv3r1RqVTo9XoWLVok1eF+GqSUnJxMhw4dqFy5Mt7e3lJFLIPBQKtWrRg1ahSLFy/m66+/5syZM5JV8bPPPsPOzk5qHxISgl1Ju4KKT88/HgmEjUCcfGZbW4EYUkzbTIEwCSlgrVatWsyYMQNnZ2fmz59PXFwcEyZMkFwRkpKSKF26NHK5nOTkZHbs2CF9/oYOHUrZsmUll5H8/HyWL1+Ot7c3jRs3LmLxz83NxcbGBiEEFStWlFwHbt68iY+PDx9//DExMTHY2toycOBASQwHBwdLKbemTZvGgAEDsFgs7Nq1S8rwMG3aNB48eMCGDRvw9fVl2rRpuLm50aJFC06cOFHs/bx37x4pKSm4u7uzevVq1Gr177Z+XrhwAR8fHxYtWlRkn8VikfLl/l00bdqUOXPm/G3n/7/K5cuXWb9+vRQU5ezsjI+PjxQUtWvXLmtQlBUrfxNWwfoKcnNzmTNnDiaTibZt2/LFF18USehfHHPnzpWSws+ZM4c2bdowbtw4qVpV3759AWjVqlWxPyzXrl3Dw8ODsLCwFy7vPc3fqdPpcHFxYceOHb/7fZ45c4bhw4fj5uZGxYoVmT17NtOmTSMmJgaz2Uz//v358ccf2bRpkxSUo1AoiImJ+VPKTC5btkwKlqpVqxYODg7Ex8czceJE9u3bh1KpRKlU0qZNG+mYrKwsDh06xMcff4yvr69UCcrV1ZU6derQrFkzDAYDb731FlqtVjpu3Lhx2NjYvPA+PnnyhJkzZ+Lh4UFiYiIzZ85ELpej0WhITU3lyJEjxMfHI5PJqFSpEgaDgVq1aqHX65kzZw75+flAwRK8i4sLI0aM4KuvvmLx4sWMGjWK9u3bS64ETy2zCoUClUqFt7c3gYGBODo64va+G+JJMSL0oEBonts2VSDqFdP2sSBoYVAha2OPHj2Ii4ujTJkyUpqvxo0bYzAYqFy5Mh9++KFUVOBZLBYLvXr1onLlyuzYsYOyZctSrly5YgMBc3NzqVixIkIIpk6dSq1atYiMjGT//v3UrFmTt99+m4yMDAwGAwcPHiQlJQWDwcCIESNQKpWkpqYCMGvWLOrUqUP58uUJDAxk4cKFkjUrLy+PJUuWYGdnh7e390szTqxbtw4PDw+6desm+UmXLl2aAwcOkJOTw8WLF/n111/55ZdfOH/+fKHyv89z48YNgoKCmD59erH7ly1bRmho6N9qdUtPTycwMFD6LFopytOgqPfee4+GDRsWCop699132bx5s7XcrRUr/yCsgvUlnD9/nsjISKpVq/bGARnt27fH2dmZ8+fPM3XqVN566y0APvroI7y8vLCxsWH58uVs27aNcuXKFdvH7t27MZlMlChRolDJzOdZsWIFjo6OODs7M2DAgD/0Q5mTk8P69etJTExEr9fTt29fNm3aRL9+/XB2dkapVOLl5cXkyZPZtm0b/fr1w9PTk+DgYIYNG8aBAwde20UhKyuLL774gqCgIIQQaLVaOnbsyJo1awoFyBw6dAghBNHR0cWKzHv37mFjY8N7772HxWLh/PnzbNy4kfr162NnZ4fRaJQKDXTr1o1BgwZha2tbJHL74cOHTJ06FVdXVxo2bMju3buZNm0aJpMJk8mEvb09ffr0wWg00rZtW/z9/VGpVPTu3Zv8/HxOnDhB+fLlqV69Ot26dcPb2/uV+Vt/+uknnJycJGu8j48PycnJlClTBrHsBSFV6QJhfm5bmkBUKb691y4vpk6dyujRoylbtiwymYyGDRvStWtX9Ho9Tk5OjB49WirZClCvXj3S0tKKjDcjI0MK/lu6dGkhQXSd60xmMs2zmqP5UoNshQzf+b7c4AYWi4UlS5ag1Wrx9vbmwYMH1KhRg/fff186/vTp09SoUQMhBD179iQtLQ03NzcMBgOfffaZZOm1WCx89tlnhISEEB8fz9atW/Hy8uKLL74oMt5Lly7RuHFjgoKCJGF95MgRyc/azc0NlUoluUwIIfD09ESr1eLh4UGNGjXo168fW7duJScnR0p3NmLEiGLv56VLlzCZTMWWEv5PYrFYKFOmjFRF7f87jx8/Zvfu3VJQlL+/P/b29lSpUkUKijp79qw1KMqKlX8wVsH6Ap5G5U+bNu13/RF7WjrPYrEwduxYhg8fDsD3339PZGQkzs7OeHt7M2nSJFxdXTl+/Hix/cyYMYPQ0FBMJhNffvnlC8938uRJQkND8fDwIDQ0lKNHj77xmJ/n9OnTtGjRApVKhUKhoHr16qSlpUnWsMqVK5OWlsbt27fZu3cvgwcPxs/PDx8fHwYMGMDu3buLWHjOnj3LvHnzqF+/vpQXValUMnv27GKtQTdu3JD8g1+Uh3bMmDHY2Nhw7969Qtvfeecd/P392bhxI0qlku+++445c+bQqVMnKdWVv78/DRo0oEaNGjg5OVG3bl0OHDjAwoUL8fT0pFGjRhw5coSYmBh0Oh02NjbUqVMHNzc3li9fztWrV6lUqRINGjTg/v37nDlzBm9vb2xtbZk1a9ZLPzvnz5/Hzc0NZ2dnhg4dire3t2TRadq0KSWOlChesBZnYZ32AgsrAs2XBT62tra22NvbY29vj4ODA506dWLFihW4uLgUycn79ddfExwcLN2Tmzdv0qdPHwwGAxMmTKBevXq0bNmSvLw89rGPxjRGTfH5YdWoaUxjpn4zFXd3d+rXr4+7uzslSpQoku2idevWaLVaVCoVKpWKxMREyf/7acWq6OhooqKi2LZtm3R9d+7ciaenp2Q9zc/PZ968eRiNRkaOHMnt27eZMmUKYWFheHp68tZbb9GyZUs6depUbBqr/Px8zp49y9atW5k8eTIVKlTAZDLh5uZGcnJysffVYrFQp04dxowZ88J7/p9k6dKl1KxZ8+8exn+cnJwcKSiqa9euREREoNVqpaCojz/+2BoUZcXK/0GsgrUYzp07h4uLCytXrvxdx9+7dw+NRkOFChWAAuE0btw4oCAYQ6fTYTab2bNnD6VKlaJatWov9HezWCw0b96cunXr4uLiQkZGxgvPm5mZSY8ePTCZTDg7OzN79uzfJbbPnz/PqFGj8PDwoEKFCixcuJBPP/2UevXqodfr6d27N/v372f9+vVSYFbTpk1Zv349WVlZ/PLLL7z77ruEh4djNpupX78+zZo1Izg4WMpDOnDgQGxsbPD39y82kAcKluZLlSqFRqPBycmJK1euFGmTn5+PwWCgXr16RfYlJyfj4+PDnj17kMlkhfYFBQURHR1N7969cXBwoFSpUsTFxeHo6IhcLsfZ2ZlWrVoxYcIE4uLisLW1lazmWq22UNL57OxsUlJS8PLywmAwMHnyZA4dOkRERAT16tUrdty3b98mKCgIs9lM9+7dcXJyokePHpI/sRACsfQFFtanPqynntnW7gU+rAjky+W4uLhIVaiqVKlCSEiIdC9CQ0NZuHBhofFZLBYqVqzIpEmTmDx5MgaDgT59+kiVszIzM0lISCBuWRxaixYZL88PK7PIEI8Fb//2No8ePcJoNGI0GunduzcPHjzg5s2bDBkyBCEEgYGBHDhwgB9++IGwsDDs7OwYM2YMlStXJjg4mLVr1xY7uenduzft2rXj2LFjxMXFERsby5EjR1i9ejXe3t40b96cb7/9Vjp2/fr11K1bt9jP3vPk5ORQrVo1wsLCMBgMDB06tIjbQFpaGtHR0a+dcu6vJisrC1dX1z9l8vpP5WlQ1PLlywsFRZUqVUpKpbdv3z5rUJQVK/8FWAXrc+Tk5BAbG8uUKVN+dx87d+7Ez8+Pbt26ATBgwACpzKXFYsHe3p6aNWuybt06zpw5g16vx2QyvXDG/+DBA4KCgujUqdMLI7Wf5dNPP5UqqCQlJb1Wcurc3Fw2btxIUlISer2ePn36SPksn+XZtFXly5dn0aJFXLx4kbS0NCpXrozBYKBt27YMHjyYRo0aYW9vj5eXFx4eHjg5OdGhQwcSExORyWR07NjxhYI6Pz9fqvL15ZdfUrt27SIpwKCgrr2trS379u0rsi8+Ph6z2cxvv/2GEKKQtTAqKgohBB06dODUqVPs3LmTsmXLEhUVxcqVK1m7di2VKlWS0jk9jbBPSkqidevWuLi4SD6Tubm5DB06FGdnZ5ydnaV68dnZ2YwYMQIXFxemTZvGxo0bmT59Ot26dcPJyalQtP7TVFZOTk5SDlbHcY4vLlfaUhRkCngkELtF8VkCEAXHDxKS6FcoFJQsWZJBgwZJ/tVRUVEoFAri4+OZNGmSVKf8gw8+QC6XU7ly5WIDmWZkzvhd+WHrbKhDq1atuHPnDi1btpSyIjwtxftsOrdZs2bh5OQklX7dtGnTCz8zt2/fRqfT4ejoyNy5c3n8+DHNmzcnIiKiWD/bU6dOUaJEiWL7ev6z2KZNG+rWrUtOTg6XL1+mXbt2lCxZUrouZ86cwWg0/uPE4ZgxY+jevfvfPYw/jUuXLrF+/XreeecdatSoUSgoasqUKdagKCtW/ouxCtbneBog8keCFcaPH09kZCQzZ84ECtL0zJ07V9ofGRlJly5dJDeBpxHL27Zte2Gfx44dw2g00qFDB+Lj419pMTh9+jRlypSRrHibN28utt358+cZOXIk7u7uxMbGsmTJkteKnH62zKqzszMNGjSgQ4cOBAcHo9VqcXR0xGQy8dZbb0nR40eOHJFKfGo0Glq2bMmnn35abIBLamqqVBYVYOLEiXTp0qVIu7Jly+Lj41PsGH18fNBoNNy/fx+FQsGFCxcYNGgQer2emjVrotVqWbZsGQkJCQQEBPDpp5+Sk5NDWloaZrOZzp07079/f+zt7fH39+eHH34gLS1NKr0qk8kwm83o9XpKlCjBmDFjGDhwII6OjsTExFCtWjW8vb1RKpXY2NhgMpmoWrUqOp0OhUIhlTS1sbFBr9dTp04dPDw80Gg0BSmzTKL4LAGIgjysDUVBHlYvUWweVoFAPBFofQryotra2hIfH09ERITk5mFnZyflv50+fTq9evXC1dUVW1tbjEYjbdq0wWw2F5kQ7GMfWrSI2QIRLRC2AtHhmfMuFwi7Z54aUWA1PlAgouftm0f79u3R6/U0a9YMe3t7HB0dcXd3BwpcXFq1aoVOp8Pf35/MzEw+//xzQkJCqFSpUpHyo+np6QQFBREfH4/JZOLUqVNUrlyZFi1avPC7kpeXh0ajeWn6p2cDzZ6vBrdo0SIpdVWVKlX+0CT3r+L69etSgY3/a9y+fZsvvvhCCopyc3PDaDSSlJRkDYqyYuX/IVbB+gzZ2dm4u7tz+PDhP9RPgwYNCAkJkXxOO3TowOLFi6X9zZo1Y+DAgdSpUwco+FH09vamcuXKL+131apV+Pr6UrduXdq3b//K5f6n+T9dXV1xdXWlV69ePH78mNzcXDZs2CBZU/v27fvGQWWXL1+WcrU6OTnh7u6Oo6MjwcHBfPjhh9y/f5+ffvqJ/v37YzabCQoKwsbGBjc3Ny5cuMD169dJS0ujdu3aODg40KBBA5YsWcKdO3eYN28eGo2mkGXoxo0bODs7F/qBOnXqFLa2toUmA0/Jy8sryGTgpmDY3WGIZQLlNiWB+wIZdncYG3/YiFwux9HRkY8++oicnBzS09OJjIwkPj6eWbNmUbJkSZo3b05kZCQbNmzg/v37HDx4kDVr1jBhwgQCAgIQQqBSqZDL5djb26NWq7GxsUGlUmE2m2ncuDGJiYm4ublJeVaFENjY2BAVFYWPjw+DBw8mJCSkkMW1du3arFq1Ct0uXfF5WF/nkSeIPhfN8uXLCQ4ORi6X4+rqilqtpmLFigwaNIj+/ftToUIFKf3U04IFtWvXplu3blSsWFF6T127duXMmTMANKZxwTL/5wKxXiBSnhOszz8+FghfgbAUjEu1ScX48eM5d+4cTZs2JT4+nmrVqqFSqahWrZrkK/v1118TExNT6L4uWbIEHx8fEhMTSU9Pp3v37nh4eEjZKgYPHoyLiws9evR45cQzMjKyWOv8U0aMGEGZMmWK+Ec/ZfPmzdjb21OmTJl/rE9kx44dmTBhwt89jJfybFBU69at8ff3x8HBgSpVqvD222+zZs0aa1CUFSv/z7EK1mdYvnw5NWrU+EN9WCwWXFxccHZ2lpbiW7RowapVq6Q2Q4YM4e2338ZkMkl/gNPS0lAoFK9czurXrx9JSUlERUUxfvz41xrTunXrMBgMhIaGYjQaMZlMxMXFvbY1FQqWvb/77jveeecdIiMj0el0tGzZkiVLlkjvMy8vj61bt9KoUSOcnZ3p3r07Bw4cYPDgwchkMjw9PaUqWqtWrZLOfffuXZYtW0ajRo3QaDTI5XL8/f25ePFioTH06NFDSgcG0KVLF1QqVbEWsk3XNmG72bbAQvlcaihFlgKRKVBsUBCdEs358+dp2bIlXl4F0fTVq1fHbDbTvn17qlWrhlqtxmQyodFoCA8Pp1GjRsTFxeHs7ExCQgKlSpXi1q1bXLx4kaVLl9K4cWOcnZ2lwgJCCGQyGWq1GltbW4QQuLq6SuLQ1tYWuVyOQqGgR48e3Lx5k6FDh2I0GqnQt0LBsv/veTwWuCS58OWXXxIYGMiGDRtYuHAh1apVw87OjsDAQEwmE+7u7gQGBiKEIDY2Fj8/P1QqlVQooUSJEoSHh0vWYO+y3iiyFYXPNfwVgrWqQIz+92uVRcWM5TNwdXWlR48eXLhwgbCwMFQqlZRa6NKlSxw+fFjKyfosmZmZdO7cGblcjp+fH/v375f2jR8/Hq1Wy8cff/zKz3VycvIL202fPp2goCDJb7c4Tpw4gVarJSoq6h/ju/o8hw4dwsPD4x8zvpycHA4ePMhHH31E165dKV26NFqtlnLlytGrVy8+/vhjjh079o+dAFixYuXvwSpYn6FDhw589NFHf6iPCxcuSAElT8Vo/fr1C/lfLliwgA4dOuDi4iJV2snMzEShUDBv3ryX9p+dnU1sbCzvvPMOnp6erF279qXtc3NzWb9+PVWrVkWhUGA0GnFycmLq1KmvtD5du3aNJUuW0KJFC3Q6HZGRkQwbNozdu3e/Mhft5cuXGTVqlCTQkpOTuX//Pg8fPmTp0qXUqlULZ2dnOiu9+QoAACAASURBVHbsyFdffUVeXh4///wzdnZ2mEwmyXJbqVIlZsyYwblz57h16xZeXl5s2bKFhw8fotFoaNeuXZFzj7k+BkWm4tWWyTyBeCRQ9C7I/6rVaqVKZY0bN6Z37944Ojoya9YsLl++jMVi4dq1ayQkJFC1alUOHDjA4sWL8fX1lcSnWq3GwcGBhIQEpkyZwoABAzCbzXTr1g17e3tkMlkhISuEwMXFheDgYCpXrsyCBQvw8fEhNjYWHx+fgjY9xBuLVtkTGRHzI0hNTcVoNGIwGApVdbp27RozZsygRIkSyGQyXFxcpOj8xMRE3nvvPcaNG0eNGjWws7MjKiqKatWq4eHhgRgkivrWvkywnhMIuUCcKTw+n9k+fPPNN4wcORK9Xo9er2fHjh1kZWUxatQojEYjY8eOxd/fv9D9vXjxIg0aNCA4OJgdO3Ywfvx4DAYDXbt2Ze/eveh0OrZs2YLJZHplJasJEyYUm+d40aJF+Pj4vPT43NxcKlSowMyZM6VqcP9UqlatWmjS/J8iPz+fkydPsnz5cvr160dsbGyhoKi5c+dag6KsWLHyWlgF6zOEhIT8YXeAtWvXUqFCBapWrSptq1mzJtu3b5de79q1i0qVKpGYmMj69eul7X5+fkRGRr7yHBcvXsTV1ZUPP/wQo9FY7JLm2bNnGTFiBO7u7sTFxfHJJ59w9+5dBg4ciLu7O+Hh4SQkJBQKcMnLy+OHH35g5MiRREdH4+zsTLNmzVi0aNELy7K+iP379+Po6IjRaGThwoU0bdoUZ2dnunbtyr59+7BYLFy5coXp06cTFRWFq6srWq0We3t7Tp8+DRS4NGzevJnOnTtjNBqJjo6mW7duGI1GBg8ejFqtLuTKkJGRQczimBcHKr3gofhf9t48Pqa7bx9/z5LZl8yaTCb7RjbZN5KIJEKExBKECu5EbQ21NxVrEVutvS1F1K5qCYqqUEuKIiixVFEU1SBByDJJZq7fH5FTkaC9n/u+nz7f31zndV7MWWbOnDmTuc77876uq4oDzUQNIiMjGRFNSUkJQkNDG1gUbdmyBZaWlnB2doalpSUjlLKysoKjoyM8PDxw+fJl5kaluLgYy5Ytg6OjI1NlZbPZYLFYUCgUGDJkCKytrREaGsokZdWTWQ6HwxBogUAA/gh+HWn9EyScX8tH4teJkEqlmDNnDnx9fdGjRw+oVCrMmjULVVVV2Lx5MxwdHZGUlIRDhw5h7ty5cHJyYmy72rdvD7VaDU9PTwwbNgzjx49HcnIy+Hx+0+4FbyOsn1CTHrH+Rf7QaDT4xz/+gfPnz0MqlTa4Ebp48SLTb3vjxg3U1tbis88+g0qlwpQpUxqQnJKSEmRlZUEgEMDf3x8PHz7EtGnT0LZt27cOI+/atQsJCQkNlm3fvh06na5RctfrmDlzJmJiYmA0GnHs2DG4urr+bauCeXl5CA0N/Y+/zr1797Bjx45Goqju3bszoqi3hTKYYYYZZrwJZsL6CrhcbpOejH8FY8eORUJCAjIzM5llERERDVTKv/76K3Q6HSZMmNDAgLxXr16QSCRMn+DbcPDgQVhbW2P16tWwsbHBnTt3GNP/9u3bQ6lUYvjw4SgqKmq07+7du6HRaBAXFwe1Wo3hw4ejV69eUKlU8PHxwUcffYQjR478y0OIs2bNApvNRlxcXAMC8uDBA8ycORPOzs7w9fXFkiVL8PTpUzx//pxJqtJoNGjRogXmzJnTgEzX1NTg0KFD+OCDD2BpaQkiglQqxcqVKzF58mQ0b94cFi0tQPOoaREQCLSFQM0JJCGQB9X1Xr6ceDU8nDbVEf87d+7Aw8MDGRkZmDNnDiIjI+uI2sv+UldXVwwYMAC7d+9GSUkJgDqyn5qaivj4eCxduhSxsbGQSqWMOKu+mioWi9G2bVsYjUYYDAYm4YuIoNFoGD/WoKCgBlVYFosFCiKwdrDq2hxeI+UW1RYQQICw+2HQddLh6dOnCA8PZwRjRqMRP//8M0JCQsDn85lY0ZMnTyI/Px87duzA6tWrIRaL0apVK6jVakilUri6usLFxQUymYx5H+y9TTgDvI2wuhJodePlujM6xqZt7969iI2NbXQt/fbbbxCLxZDL5XBwcEDLli3f6FlcXV0NhUKBPn36QKlUYsKECQgMDHxr5fPGjRuwt7dnHufn50Oj0bw1NQuoI9NqtRq3b98GUNcKFBQU9Fbh5P8mamtr4eTk9M4gi7+CelHUtGnTkJSUBJ1OB41Gw4ii9u7daxZFmWGGGf82mAnrK+Byuf/jPq+oqCi0a9cOy5cvZ5YFBgY2qIIajUYIBAJs3ry5QXWnb9++iI2NxZQpU/7Ua+Xk5CA8PJwRmVhbW6NVq1ZYt25dI0Xzq6996tQpjBw5EhKJhKnktWrV6o0Z7H8W1dXViI6OBpvNxty5c9+4ndFoRH5+Prp3784ow8ViMVasWAGj0YgjR44gIyMDCoUCrVq1wtixY7Fw4UKMGTMGnTt3/mOo/JXZwcEB7pfcQduoaRHQParzLt1HdcKfPVSnXC+uW88CC23L2iIxMZFR9bPZbPB4PEilUuj1euzatauRwT4APH78GCtXrkRsbCy4XC6USiWaN28OgUAAFosFCwsLREZGYvLkybC0tIS7uzt69uwJS0tL8Hg8CIVCTJkyhXEPkEgkzPvicDgIDAyElZUVoqOj0bJlSwjthWCPY4O1ngXaTaC1BBpNUDZTws/PD7a2ttBqtdBoNOBwOGCxWBCLxQzhrPdjlUql8PPzQ2xsLJKTk/Hee+/Bz88PgYGBmDRpEiMMVKvV4HK5YLFYaNmyJeS75X+esH5PdU4GZY3XpeGPdo7s7GxMnDix0bl9+PAhE7nr7u6OoKCgNwoEz549C29vbwDAzZs3kZaWBpVKBZFI1OSNG/CHU0BZWRlOnjwJtVqNY8eOvfU6NxgM8PPzQ25uboPlkydPxvjx49+67/8mFixYgNTU1H9p3/LychQUFGD+/Pno1asXXFxcIJVKER0dzYiibt++bRZFmWGGGf8xmAnrK+DxeH9ahNQUampqIJFIEBgYiO+//55Z7unp2egH09PTEwcOHIBWq2X+yPfq1QtTp06Fs7PzO//wV1dXY9u2bYwwxtvbG1FRUU0OST5+/BibNm1CWloaNBoNPD09MWbMGHz77bcYO3YsdDodEhIS4Orq2sAQ/6/gypUrUKlUkMvl76xO1cNkMqFv374QCAQQi8WwsbFBdHQ02rVrh+bNm4PH40GpVEKlUsHCwgItWrRAVlYWPD09wWazYWVlhbCwMCiVSkR0jWhoAfU6gfqB6myiXp3UBDrxyuMKAtuKDV9fX+Tk5GDz5s2ws7PDRx991Khnt6SkBLm5uYzLQVRUFFq1agWhUAgLCwuwWCymkunn54cuXbqAy+WCz+dDIpEwIisOhwN3d3cIBIJGJLzegUCr1YLP5yM0NBSxsbGwsLCASqVCcnIyWrduDQcHB7DZbDg4ODSoBEulUvD5/LqYVyL06dOHuZGprKzEJ598ApVKhWnTpjEjC9evX4dGo0FlZSWKi4uxfPlyCIVCcDgcWFhYgMPhQDBRAHbVyyprDdWd9ywC9Xn5/5pXzun7VBdq8NrEq+VhtnE2cz6jo6MbtM0AwJEjRxgnhvoe4pUrV0KtVmPSpEmN+h6XLl2K9PT0BssuXboEHx8f8Hg8LFu2rMkbUn9/fybx689EmU6cOBGJiYmNvqNvqhL/XfDs2TMoFIpGYsbX8aooKiMjo5Eoas2aNWZRlBlmmPFfh5mwvgJfX99/mbABdcOE7u7ukEgkDWJEnZycmL7MenTq1Anbt2+HRqNhfkC8vb1x5swZeHp6vrHKc+vWLWRnZ0On0yEiIoLpkaz3Ex0xYgSMRiPOnj2LadOmITw8HDKZDJ06dcKyZcuYIcxX8c0338DKygo9e/aEVqvF9OnT/9KP0fLly8HhcBAaGtpkZddgMODatWvYu3cvFi1ahGHDhiEhIYFJdBIIBIiOjkZiYiITo1jfW1lPCh49eoQRI0ZAKpWCiGBjY4OCggKGxIimiBoOk79OWGsJFEWgXS//n0cgPTUQM3EMHPQ43QOfffYZOnTowBxHRkYGevbsifj4eDRr1oxJw6pXzdeTy3oSqtFomMpqvQsAi8WCj48PevTowVTD622k6qufISEhkMlkzHtUKBQoKSmBr68v0+v8yy+/QK/Xo1mzZrh69SoyMzMxb948hIWFIT8/H0BdpdHLy4tpJbC2tmbcF/z9/ZGdnY2tW7fi+vXruHnzJrp06QInJyfMnTsXCxYsgJWVFTQaDXNcCoUCGzduxMOHD1FYWIjYXrF/OC9MbkyyafLLdZUEkhPoYGPCyqpiwcbXBuPHj0dRUVGD70xpaSkGDBgAvV6PvLw8cDicBjcM9+7dQ1JSEjw9PRsMcU+YMKHJWFSj0YiAgAC4urrC1dUVmzZtaiA4TE5OhkKh+FOipDNnzkCr1TaZXnb58mU0b978nc/xv4nhw4cjKyuLeWw0GvHTTz9h/fr1jChKJBLB09MT/fv3N4uizDDDjL8N3kZYufT/M4SGhtKpU6coJCTkX9r/1KlT5OXlRRUVFWRpacksr6ysJIFA0GBbV1dXunnzJgUGBtLZs2dJLpfTL7/8Qr6+vtSvXz9au3YtRUZGEhFRTU0Nff3117RixQoqLCykPn36UH5+Pnl5eRERUVhYGMXGxtLIkSNp7ty5tHr1atLpdJSQkEBTp06lyMjIRq//Ktq3b09nz56l3r17k16vp9WrV9PSpUvJ2dmZfv31V3rx4gWVlpaShYUFqVQqcnR0JA8PD2rWrBnt2rWLTp48SVlZWZSWlkb5+fl048aNBvP9+/fJ1taWXF1dmVkqlVJBQQG5ublRYWEhyWQy5ngePXpE69atoyFDhhCXy6Xo6Gi6cOECPXjwgMLDw+no0aPUrVs36tWrF5WWlhKPxyOn75zosujymz8cDhH1JaLeRFRFRDwi2kpE4j82MfKM9EPFD3Tq01NUXV1NgwcPJpVKRTdu3KBr167RtWvXyN/fn1q0aEF37tyhq1evko2NDd27d4/atm1LgwYNIh6PR3379iUej0cGg4FCQkKoqqqKunTpQjdv3qTdu3eTXC6nFy9ekI2NDRUXF5PBYKD58+fT9OnTycrKiuzt7eno0aPk7+9PAQEBJBaLKTk5mYiIzp8/T/7+/nTy5ElSqVRUVFRESUlJVF5eTnv37qXY2Fi6ceMGPXnyhHlfixYtopSUFDpw4AD16dOHCgsL6eTJk3ThwgUqKysjPp9PlZWV9PHHH5Narabo6Gg6ceIEPXnyhNq2bUvbt2+n3bt3U3JyMt27d48GDx5MxfeK6ZLzJaIpVDc3BQERPW1iuYko6GEQLVy6kHbs2EHR0dFkNBpp48aNJBAIaOLEidSlSxe6fPkyyeVyEggEVFlZSVKplIiI9Ho97dy5k7Zu3UpdunSh1NRUmj59OhERsVisRi/HZrNp+/btFBwcTDk5ObRw4UKaPXs2zZgxg/z8/KigoIACAwMpNTX1zdcPEVVVVVHfvn1p4cKFpNPpGq1v6rX/TgBAKSkplJiYSDU1NfTjjz/S2bNnydLSkoKDgykkJIS6du1KAQEBzLk2wwwzzPg/gTcxWfw/WmHduXMnQkJC/nIfVjGKMRuz4XrSFfYX7KE7pMNszMZD1Pk3WlpaMuKceixZsgSDBg1ievc+//xzdOzYEUCdJZRCocDly5cxfvx4WFtbIyIiAuvXr2cqmCaTCT/++CNycnIQGRnJDKtnZWVBpVLh22+//fPHX1yM2bNnw9fXF1KpFEKhEKGhoZDJZFiwYAEePXqEp0+f4u7duzhw4ACmTZuG9u3bM0b4bDYbbDabiYIdPnw4Fi9ejH379uHnn39u1Pd5+vRpyGQyqFSqNwrMampqsH79ejg7OzPD7D4+PuByubC0tGT6M+uHqWn3O3oq8wmkJNAZAhkJdJpA1gQ6/9p+u+piTPV6PdRqNSwsLODs7Iy4uDhERERAIpHAxcUFjo6O0Ov1mDp1Kvbu3YvRo0fDzs4OMpkMTk5OsLS0xJEjR2BpaQmVSsVUWvv06YMNGzZALpdDIpHA2toaQUFB4HK58Pf3R1paGvh8PsaMGYOamhrodDrI5XJMnz4dNTU1mDBhArKzs8HhcFBdXQ2lUokHDx7g7NmzsLOzQ1hYGGNB1aZNG9ja2kIsFmPMmDGYPHkyWrduzQQmdO/eHZ988gkWL16MOXPmIC0tjan8EhH0ej2TzBUREYEdO3bg0qVLiIqKgk2yzV92Y2CqqxUsuPRwgUQiQYcOHdCjRw+0atUKNjY2TBRsXl4ec92o1eo3eqE+fvyYiUnt169fg+rh61i1ahX8/f1RVVWFvLw8NGvWDCKRCPHx8UyIx9swZswYpKSkvPHvQ2FhIXx9fd/5PP8tlJSUYP/+/YwoytraGhqNBlZWVujYsSP27t37Vo9ZM8www4y/E8jcEvAHamtr4eLi0ij28U04jdPogi4QvJxe/VEWQggBBOiCLrBoadFoqHz//v2IjY3Fjh07kJCQgGbNmuG7775DdXU1tm/fDpVKBYlEghEjRuDy5csA6nrQtm/fjoyMDNjY2MDFxQXDhg3DN998g4qKCgwYMAA9evTAkSNHoNFomP3eBIPBgE8//RRqtRoZGRk4fPgwSktLsWzZMigUCgQHB0MmkzFD2AKBAB4eHggICACbzYZOp8P27duRn5+PiRMnwtraGunp6UyQwOswmUy4ePEiFAoFxGIxpkyZgoULFyIrKwv9+vVDu3bt4Ovry5juczgc2NvbIz4+Hj179oSrqyszzB0aGorvvvsOT58+RW1tLcKuh72dsM4lUOfXqFPyy+WvTMo9SkgkEkRFRWHIkCFITk6GWq1moma5XC6kUinTh1ovzHJycoKjoyOsra0hFovRq1cvxoyfiNCuXTuUl5fDaDTC29sbPB4PNjY2+Pbbb6FUKuHi4gKBQMDYiZlMJqxZswaRkZG4c+cO2rZti+DgYERFRWHt2rVQKpW4f/8+1Go1rl+/jm7duoHNZiMnJwdbt26FSCSCl5cX46jA5XIxYMAA5OXl4cKFC/D398cHH3zQYGj82bNnCAgIAIfDYfxzHR0dERoaynxmXC6XEXW9f/Z9iEyiv0RWhSYh+v/QH7169YJCoYCdnR3Td2tvb4+pU6di1qxZiIqKglqtRmZmJrRabZOtLK9i3759UKvV0Ov1KC0tfeP1l5iYiIkTJ+L58+cICQlBhw4dYGtrC4FAgLNnz77x+QsKCmBtbf1Wgvf555+jX79+bz3O/xRevHjBiKJSU1MbiaK2bt3KiKIOHToET09Ps0DKDDPM+D8FM2F9DatWrUJQUFCTavBXsRRLIYIILLDeXk0ysUAvCEtNDQMBbty4AQcHB9y5c4dRa2dlZcHa2hqRkZEYOnQoYmJiUFRUhNmzZyM6OhoSiQTt2rXDokWL8PPPPzc6psrKSgQEBGDhwoVYu3YtnJycmvyBLS0txY4dO6DX6+Hm5obk5GS0bNkSWq2WSXNKSEiAvb09XF1d0bZtW9ja2uLw4cPo06cPWCxWA9sug8GAu3fv4vDhw+jcuTPEYjFSU1MxdOhQdO3aFeHh4XBycoJQKGS8SN3d3dG9e3cMGzYMM2bMwMqVK/Hxxx/Dzc0N3t7e2Lp1K0OmSkpKMHbsWKZvdOfOnejTpw/kcjni4uLqSM9Efl3F700ioCMEUr1SUT33suL6bcPKX8oPKVi5ciXi4+OhUCgQExMDX1/fOoL2/vvIzMyEm5sbnJycMG7cOOTn5+P8+fNIT09nekWFQiFD+BQKBZRKJYgIMpkMcrmciWZNSEiAWCxGREQEXF1d0bVrVxAR0tPTYTAY4OjoyNihmUwmLFu2DGw2G4MHD4arqyu++uorhiC3bNkSGo2GEar5+fkhOTkZAwYMwM6dO6FSqeDh4cGIjp4+fYrWrVsjNTUVBoMBFy5cgFQqBYvFgqOjI6ZOncpE7YaEhMDNzQ0tW7aEi4sLmjdvjqSkJDg5OYE1hPWn/WGpnBC0Kgi7du1CVVUVzp07B09PT3A4HDg4ODA2YFKpFD4+Phg5ciQ++OADpsqdk5PzViP/GzdugM/nQ6fTYceOHU1u89tvv0Gj0SA4OBgZGRkwmUyorKwEj8eDtbU1UlJSGKuterx48QIuLi4NPJObQn3P538a1dXVOHv2LJYvX95AFBUSEoIPPvgAa9aswZUrV94YDGIymeDj44MDBw78x4/VDDPMMOPfBTNhfQ0mkwkdO3ZsMv2mHvVk9a9UlkQQYSn+IK01NTXg8XjIyckBEUEul2PkyJE4c+YMdu7cifT0dLBYLNjZ2WHo0KHYs2cPXrx48c7j/+WXX6DVarFnzx7069cPbm5uyM7OxnvvvYfQ0FCoVCoIhUJwuVwEBAQgKysLq1atwtGjRxkldv15ePjwITIzM6FQKNChQwcmpal169aIi4uDl5cXVCoVuFwubGxsEBAQgMTERCQnJ0MikaB79+7YunUrvv/+e1y5cgWRkZGwsbHB2LFjG5yHDRs2wMPDA6GhodizZw9zDC9evEBOTg7UajWSkpIgk8kY26B6CyMmOUpDdUKgyfRmEdBnBHKhOh9WJwJ9+tqn9NIlwMPDAx06dIBarUZ4eDi6d++O5s2bw97eHmPGjMGZM2eYY3z+/DkGDBgAPp/PKP7ro01ftTY7f/48+Hw+WCwWgoODERERAbFYjMDAQIjFYqZqXW89xePxIJFIEB8fj7S0NIwZMwZZWVng8XgNYl85HA4SEhIwc+ZMTJkyBS1btoRCocCjR48wa9Ys5lwPHToUGo0GGzduZI6psrISMTEx0Gq1IKprhdiyZQvz3mJiYhAXFwepVMq0nEyePBnFxcUYPXo0lEolRo4ciUXHF8Gx0BFUSWBVNryBY1WyYFFrAW2BFulL07Fs2TK0atUKAoEAAoEA6enpTETxr7/+iuXLlyMxMRFCoZBph+ByuejQoQM6d+4MpVKJmJgYrF27tslI3nr7MHd3d6SkpODBgwcN1tfU1CAkJARSqZTZvxjF0C/UI+5BHJr/3Bz8r/gI2hKEc3fr3C4++OCDJhPVXkVpaSkUCkUD7+B/B14VRQ0bNgxhYWFM9bx///5YunQpzpw585dFUbm5uejQocO/9VjNMMMMM/6TMBPWJvDo0SM4Ojpi9uzZjYbNTuM0hFVCUDqB7F+SH1+q8/cEgS5TnXG95cs59uWyl6T1DM7g5s2b+Pjjj8Fms2FhYQFra2vGg1UikSAuLg7z5s1DSkoKZs6c2eQxmkwmPHjwAAUFBfjiiy+QnZ2Nnj17IjAwECKRCCwWCy1atICdnR28vLzwxRdf4Pvvv8fBgwehVCoxZ84c5OXlYdmyZZg0aRIGDhyIpKQkhISEwN7eniFGzZs3h5OTE0OinJyc4OzsjNWrV+PixYt4+PBhk5WcmzdvwtXVFStXroTJZEL//v1hZ2eHTp06oba2FtXV1Vi9ejVcXV0RGRmJAwcOMOfaYDBgyZIl0Ol06NGjB65du4Zu3bpBKBRixYoV8PT0BIvFYsgyn89H586dYXXc6t2VvrdUAC12WzCq//oQA51OhxEjRuDEiRPM+zQajTh06BD69u3LxJn6+/vD2dkZGo0G7u7uDTxFHz9+zPSCikQiCIVCqFQqTJ8+HVqtFmfPnoWnpydCQkIwb9483L59GzweD87OzsjIyEBERATTs8vhcBoQ23oXAF9fX8TExIDFYsHPzw+ffvopevfuja5du6KoqAh37tyBnZ0dHB0dmYCJuLg4xvNVLBY3qF7u2bMHarUaWq0WkyZNglarRevWrSGVSiGVSjF06NAGSvlOnTph0aZFaH+oPfSH9Ih+Hg2f8z6QfSJDTM8YxjnB09MTcrkcLVq0wMCBA+Hs7Awej4ehQ4fi2LFjzDmurKzE/v37kZmZCQsLC4hEIlhaWkKhUCA2NhZhYWGQy+VIS0tDfn4+42qRl5eHoKAgVFRU4OOPP4ZWq8WaNWtgMplgNBrxj3/8A/Hx8UhJSUGv+b2Ylh5OFafB9cCt5oIqCbqTOqgT1G9sM6jHzJkz30lq3wWTyYS7d+9i+/btyMrKQmxsLORyORwdHdGjRw/MnTsXR44c+bckRVVWVkKr1f6PvZfNMMMMM/5bMBPWN+Du3bvw9vbGsGHDGrQHdEGXuiHQyQS6RXUCnq9fEtdbBHry8l/Ty2HQRQTyeVltMrKgPaaFSqVCy5YtwWazIZfLIZPJEBgYiF27djWoGh09ehTu7u44fPgwVq1ahaysLKSkpMDX1xdisRgqlQphYWF47733MGbMGOTk5GDRokVYsmQJYmJiYGtri27dukEqlUKtVkMulzM2RSEhIUhKSsKgQYMwefJkLFu2DHl5eTh58iRu376NyspKmEwmZGZmgsViISUlBfHx8WjZsiWmTJkCtVqN1atXv7UP7sqVK0ySll6vh7e3Nx49eoTly5fD0dERMTExOHLkCLO90WjExo0b4ezsjHbt2jE9hYWFhbCwsGBsoupFTEKhEO+99x7u37+PY8eOQdlOCYtqi3+NsL4gsIJZcHBwgFwuh1wuh1gshkgkwgcffIBr167h559/RnZ2Nuzt7eHr64sPP/wQSqUS8fHx4PF46N27NwYOHIgOHTowxOvq1atwdXVFhw4d0KJFC/Tr1w9EhMzMTKjVahQUFKB///6Mn+vw4cPh7OwMDocDpVIJqVSKCRMm4IsvvoCdnR00Gg1Gjx6N/v37w8PDA82bN0dkZCT27duHLVu2gM1mo1u3bhgxYgQCAgJga2sLT09PqFQqsNlspiorl8thZWUFNpuNkJAQJCYmwsHBAVu3bmVsrrZt2wY+nw9vb29kZ2dDq9UyHrlt2rTBpUuXANSJ9uRyOS5fvgylUombvht4/wAAIABJREFUN28CAMrKyiAWi1FdXY2SkhLGb7Zfv35477334OXlxdiA+fn5wdraGiqVCkOGDGHiewEwfbvz5s1DaGgoeDweZDIZ077i7OwMvV6PrKwsFBUVISgoCJ999hkA4Ny5c/D390fbtm2RkZGB8PBwvHjxAnOfzwWVv2zZeceNDJUTEnYl4OnTp01e59euXYNarX5jAteb8KooqlOnTrC2toZWq0ViYiKmTp2Kffv2/UdFURMmTMDQoUP/Y89vhhlmmPHvhJmwvgVPnjxBYmIimjVrhn379qEYxY3EVczkQ3UpS69ONQT6J9UlKr2cWFUscKw54PP58PT0xLhx47Bs2TKEhoZi+fLlTJqTt7c3+Hw+2Gw2XFxc0Lp1ayQmJqJz585ITk5G27ZtERAQABsbG1hYWECtVsPb2xtxcXHo06cPRo8eDXd3d3Tq1Albt26FTqdDx44d0bVr1z8ltnj69Cm8vLzA5XKxadMmAHWEcubMmdBqtfjnP/8JHx8fpKSkNHJAeBXvv/8+uFwuNBoNpkyZAltbW7Rv3x7Hjx9ntjGZTNi7dy98fX0ZMVVtbS327duHzp07Mz6mQqGQMd5PTExkqkNffPEFNBoNvv76awz6cdBfV6+/INAgAhEhIiIC33zzDWpqarBv3z74+/tDJBIxjgSdO3fGuXPn8PPPP0Or1cLNzQ1cLhdfffUV1q9fDxcXF6Yat3//fmg0GixatAharRZr166FQCBA69atwWKxEB8fj8DAQHC5XAgEAri4uGDChAmwtLRkzs/8+fPB5/OhVCoRHByMtWvXYvbs2Rg1ahSEQiFKS0sxY8YMaDQaJCcnIzg4GIMGDQJQZ6Q/aNAgnDp1CmlpaQwRr68CczgcdOzYESNGjEBqaio0Gg0TVsDlchu4MNjb2yMjIwPz58/HunXrkJmZCUtLSwwZMgQzZsxAWloaUlJSMGnSpAafv5eXF2bMmAFra2sMGDAASqWSIboAEBAQgNzcXKxatQrDhg1DYGAg014hEonQsmVLuLu7Y+XKlcxNwLNnz7Bt2zb06NEDUqkUIpEIXC4Xer0elpaW8PT0hFgsxqFDhwDU9Xy2bdsWHA4Hs2bNwhLjkr/c0sOp4kAyRoI5c+Y0CBeprKyEv78/li5t2KP+OupFUfPmzWsgimrTpg3GjRuHrVu34s6dO/9VIVS9G8m7qsdmmGGGGX8HmAnrO2AymbBnz546oc1SJ1jUNFHB+51AfAJdfWWZnEAcArEINO2V5eUE60+t6yIuX1Y86xXnryrN64eOFQoFrK2t0bVrVwwdOhTTpk3DypUr8fXXX+PMmTO4e/fuG+NkHz16BHt7e+zcuRMnT54Ei8XCV1999c73fPToUYhEItjY2DSpzi4oKICdnR0+/PBDDB8+HHZ2dgw5eBXHjh1j3qNMJkNSUlKDiFoA+P777xEZGQkPDw/k5eXh3r17mDZtGpMQFRQUxPSiKpVKREREMOEORqMRY8aMgbW1NTp16gRLS0u0adMGFsMs/rQQiF3Jhni0GF26dAGbzUZ4eDgCAwOZKFCJRILWrVvD398fCoUCer0eDg4OEIvFEAgEEIlEOHPmDM6fPw+1Wo2ioiKYTCYsWrQI1tbWOHLkCNq1a4fw8HAIBAJGjKXVapn9N23aBIVCgd9++w3Tp09Hr169UFpailGjRkGpVGLIkCGws7ODQqHAxYsXMXbsWIwePRpOTk7MeTxx4gQT3qDT6VBRUYH+/ftDrVYzoQCPHz/GyJEjweFwwOVymaCB69evIyYmholklcvlTNuCn58fxGIxli9fjpycHHz44YdITU1FdHQ03N3dGYV/vdAsPj4e/fv3x0cffYSJEycySWWbNm3C48ePMW/ePCaO+Pnz5xCLxUzK1qvfuZs3b2Lu3LnMSES9c4G/vz8GDx6M5cuX4+TJkygrK8OZM2fw0UcfwcnJiWmZEIlEICKEhoZiwIABcHZ2RkFBAfze9wPrCevNLT0nCRRHIAXVJaGlEOi3unUCowBtxraBjY0Nli1bhuLiYkRFRaFPnz4NiOaroqj09HT4+Pg0EEWtXbv2raKo/yb69Onz1hhlM8www4y/C8yE9U/CYDAg6k5UY9JTTXV9qgPfULlbQnW59a9WazZyoFKpoFQqwefzoVAoGoiENBoNAgIC0LdvX4wdOxYymexf7lv74YcfoNFoMHXqVISHh0On0+HWrVtv3D47OxtsNhtJSUlvTbt6/PgxEhMTERoairVr1zJiqvr2ibNnz0IsFjORoa1bt26w/4ULF9CxY0fY29sjNzcXX3/9NTp37gxLS0u8//77SE9PZyptbDYbWq0W+/fvh8lkgsFgwLZt22BnZwcul4uWLVtiyZIlePDgAcrKyuoU+TEysHawwKpiNa64lhOogmBzwgafnfwMM2fOhLOzM3Q6HSwsLBiVevv27WFrawsvLy/MnDkTe/fuRUxMDGMTRURITk7GDz/8ACcnJ3z55Ze4c+cO4uPjGcGWQCAAl8sFj8dDeno6hEIhevbsiYqKCtjb20MmkyEzMxMZGRkoLS2FSqXC+PHjodFoMHDgQEY0dOPGDbDZbCQmJqJPnz4YPHgwkpKSmPM5b948dOvWDUOHDmV6Un18fBATE8N8jkeOHAGPx4NAIIBKpcKhQ4cwa9YsqFQqfPrpp3jx4gU6dOjAVFcXLFgAAGjXrh3Wr1/f5HVw4cIFxp3A0dERixcvxsqVK5GYmAiBQAC9Xg+tVovmzZtDoVAwFVtXV1eEhIRAq9UiKysLCxYswObNm/Hdd9/hypUrKCkpYUhgjx49MHnyZAwYMICxwgoODoa3tzeEQiGaNWuGHj16ICcnBxs3bsSsWbMQFhbGtEDU9yT36tULUY+jQGX05paefQT6ikDPXl4n/yBQu7rrhgUWuqIrTp8+jfDwcFhYWCA+Ph6XLl3CunXrGomi/vGPfzCiqHe5jvxv4cyZM7C3t28UPWyGGWaY8XfD2wgrq2590wgKCkJhYeG/GEnwfxOdqBPtoT1/LDBRXXJSGRHtIiKLJnYyEZGGiK4Skfblst1EnK4cIiIyGo3MpiwWizgcDnE4HCY1BwBVV1cTEZFQKCSJREIikYjZ7s/MDx48oOvXr5OXlxcZjUb69ddfqU2bNiQQCJhtiIj2799PJSUlFBERQS1atHjn87LZbDpx4gQdOXKEkpOTqaioiEpKSsjR0ZFOnDhBfD6fwsLCKDU1lUaNGkWrV6+m0tJS+uqrr+jChQuUmJhIRET5+flkaWlJSUlJZDAYKDc3lwwGA2m1Wnr06BEREe3du5fu3LlD+/fvp/z8fKqtraXmzZvT559/To6OjnT8+HHauHEj7dy5k2praykqKoquXr1Kj/CIqB+Rso2SHP0cqbl1c3Ipd6GF/gvpk2GfUG5uLt2/f5+qqqqYNCVbW1uKioqif/7zn8Rms+n48eO0du1a2rhxIxkMBtLr9fT06VMyGo3k5OREP/30EwkEAhIKhfTs2TNSKBSUkZFBAQEBNHToUKqtraVVq1bRV199Rfn5+fTNN9/Q6tWrqaysjGJiYmjw4MH0zTff0Oeff075+fkUERFBc+fOZZLMiIgKCgpo9OjRFBISQmvXrqWIiAgKDAyk6dOnU2VlJdnZ2ZG3tzddunSJFAoFlZSUkLu7O+n1etq+fTt9/vnnNHz4cFKpVNSlSxdyd3ensWPHUkREBOXm5tLp06cpIyODOBwOrVixgjQaDaWmptKKFSsIAM2fP58KCgoaXd5jxoyhc+fOEZ/Pp+TkZBo/fjxZWFiQu7s75ebmUlVVFXXv3p2uXbtGREQGg4HWrl1Lc+bMobCwMHr8+DFFRUVRcXEx/f7771RcXMz8v6KigrRaLVVWVpKNjQ0FBweTRqOh58+f09WrV6mwsJBcXFwoOjqaXFxc6Pbt2/Tjjz/Sjz/+SGw2m3Q6HV29epU4HA5VV1eTSWUiukNEwia+py2IaDIRdXtt+Tkiak1Ez+se8kw8inwvkk7sOkFqtZru379PHA6HQkJCqHPnzhQSEkIBAQEkkUiaeJG/JyIiImjkyJHUrdvrb94MM8ww4+8DFot1FkBQk+vMhLUh+lAf2kgb6x6AiNKJ6DYR7aOmfwSJiGqJSEpEJ4jIv26R/yV/mls8l9zc3Mjd3Z1evHhBEydOJAsLC8rOziaj0dhgXr9+PeXl5VFkZCRt2LCBXFxcKCsri+zs7Bpt29RcW1tLSUlJFBgYSGPHjqXPP/+cfv/9dxo7diwREd24cYNmzZpFHA6HxowZQ1qt9k89b/1879492rNnDwmFQnry5AnV1taSQCAgrVZLUVFRZDKZaNeuXaRQKKi4uJisrKyourqaSktLydramnQ6HZWXl9P169eppqaG+Hw+GY1GhnhxOBwyGo3E4/GIz+dTWVkZCYVCAkAGg4FMJtM7Pzs2m01cLpcsLCyIy+XSixcviMViMc/LYrGoqqqKiIisrKzoxYsXxGazycnJiaqqqujOnTtkMpmIxWJRTU0N1X83WCwWASCRSESVlZXE5XLJ29ubXF1d6YcffqDffvuNoqOj6dGjR/Tw4UPi8/mkUCjo1q1b1LdvX/rpp5+osLCQysrKyGQyUbdu3SggIKDRzcGxY8fo4cOHlJaWRuPHj6fS0lKKi4sja2tr2rlzJxkMBkpPT6c2bdrQlStXaMuWLSSRSJgo13PnzlFERATJ5XK6e/cu3b59m5o1a0ZGo5GeP39O165do44dO9KiRYuIz+cTh8OhoqIi6t27N02YMIFycnJo37595OPjw9xQ1dbWko2NDdXU1NCxY8do48aNlJubS15eXnT16lWaOXMmpaWlkVKppF9//ZUUCkXdVweg1q1b05MnT2jatGnUuXPnJj8zg8FADx8+pBEjRpBWq6WgoKAGpPbBgwf0yy+/0MOHD5nrRqfTkbu7O9XU1NCJEycoKCiIamtr6dq1a/R0wFOiqUQkeu2FionIgYh+JKLmr61bSERfEtEPLx9XEMkXyGlQ2SCKjo6moKAgOnnyJGVnZ5NUKqWcnByKjo5+5/X4d8K2bdto0aJFTd6QmGGGGWb8XWAmrH8Bc2gOTabJVEVVRIOp7gfuIBG9WkzJJyI11VVsyoloAhFtI6JfqC5bvYJIuUhJ+s16unv3LpWVlVF4eDgpFAq6e/cu5ebmkqenJwmFfzDgsrIysre3p+vXr5NMJqNFixbRnDlzaMKECTRixIh3HndVVRXJZDJyc3OjDz/8kNLT0ykxMZHc3NzIwcGBsrKyKCoqir799lvi8Xh/6ZwUFxfTvHnzaNWqVSSTyejJkyekVqvpwYMHFB0dTYsXL6Y1a9bQ7NmzycXFhamaDRo0iHr27ElHjx6lzMxMunfvHjk6OtLTp08pMTGRWCwWffnllwSA0tPTKTs7m/bv30/Dhw8nDodDXC6XDAYDtWzZkpydnen777+nn376iYiIJBIJcTgcSk9Pp88++4yGDx9Oer2eNmzYQFeuXCEApNfryWg00oEDB0gkEpHRaKTBgwfTwYMHSSqVEo/HIy6XS48fPyYAZDKZiM1mk6WlJUmlUsrIyKBjx47RoUOHiMPhUE1NDXG5XPLw8CAA9NNPP1F1dTWlpKQQh8OhI0eO0KhRo2jfvn104sQJyszMJC6XSwsXLiQLCwsSCARUXl5OgwcPJoFA0Oim4ODBg6TRaMjd3Z22bNlCVVVVZDQaSSaTUXV1NQUFBZFCoSCj0UjV1dV0+PBhcnV1pRs3bpDRaCSRSEQ2NjZ08+ZNkkgkpFar6dGjRwxx12q1xOPxGr1uTU0NlZeXMxV/ImKIO5vNJqPRSBwOh0wmE3G5XJJIJGRhYUEmk4nKysqYfTQaDUmlUoaAGwwGunbtGvn6+r5zxODKlSskEonIw8PjjdvU1NTQzZs36fr16/TgwQMymUxka2tLGo2GDAYDlZeX02+zf6PqHtUNL+AaIkogIhci+vy1i/siEUVT3ehJ5B+L0yiN1tG6BpsajUb68ssvadKkSeTi4kI5OTkUFNTk39W/HWpra8nFxYV27NhBgYGB/9uHY4YZZpjRJN5GWM09rK+BcQm4/bLflE8g8Svzhpf9b81ePlYTqAOBLrzSP1lBYGnrPDTZbDYEAgFiYmIwaNAgCAQC+Pj4QCAQoFmzZkhJScEnn3yCvLw8JCUlMT2FAHD79m14eHhg1KhR7xRvVFZWgs/n46effoJarcaZM2fw6NEjxq911qxZf/lc3Lt3D8OHD4dCoUBmZiZ+/fVXZGVlQavVgs1mY968eYiOjgaLxWIiTdu2bYvCwkKYTCZ8/fXXcHFxYaI+JRIJvL29oVKpEBISgsmTJ0MsFsPR0REVFRUIDw9nFOwBAQEYOHAg00fI5XIRGBjIiLs8PDzQq1cvLFy4EGw2GyNGjICXlxecnJwwatQojBo1CiqVCmKxGD/88AOqqqpw8uRJdO/enRGJWVhYMKEEHA4HWq0Wo0ePRmhoKCoqKnD58mWo1WqMHj0aarUaAwYMgIuLC5RKJQQCAYgIPj4+kMvl4PP5+OKLL1BeXg5XV1dYWVlh9OjRkEgksLOzw7Vr16BUKjFq1Cj4+vo2qdpu0aIFcnJyGIcBLpeL06dPw9XVFSqVqlGPc5s2bSCVSsFms7FkyRK4ubkxSWGpqalQq9Xw8vKCj48PFApFk8lp9bh79y5cXV0hFApRXl4Ok8mE2tpa5vzr9Xps2bIFJSUlePjwIR48eIB79+7h1q1bmD17NgQCAby8vHD8+HFcvHgR58+fx5YtWyAUCtG/f38UFBTgyJEj+O6775Cfn4/9+/dj79692L17N/Ly8tClSxf07NkTmzZtwvr167FmzRrk5uZixYoVWLp0KebPn48pU6Zg9OjR6NmzJwQCAZycnJiAhXpxG2vPaxZWRgL1JFDCy170V6frBLIh0LrGvekd0fGN58pgMGDZsmWwsbFB165d/7LV1f8W5syZ8z/2kTXDDDPM+E+CzKKrv4Yu6PLOONY3TSwTC60etGJ8O+ttq141go+NjcWmTZtw6tQpbNy4EVlZWUhMTIRGowGbzWZUz4sXL8auXbsQEhLy1lQuAKiqqgKXy4XRaMS2bdug0+mgVCohkUigUqmwb9++P/3+b9++jSFDhkChUGDUqFGMefzKlStha2sLpVKJ7t27M6TKwcEBCoUCDg4O2Lx5M7Zt2wZ3d3eIRCLIZDLmPPj4+GDOnDmMIGzatGlwdnZGXFwcOBwOWCwWoqKiEBcXB4FAAKlUCq1Wi08++QSPHj2Cl5cX2Gw2IiMjMX/+fPj5+cHV1RVEhL59++Lo0aMwGo0wmUy4fv06PvvsM6hUKnA4HAgEAvj5+SElJQWWlpbg8XjgcDjYunUrVq9eDZFIxHxOY8aMwaVLl+Dm5obWrVvD29ubOWaTyYQvv/ySEYpxuVyw2WykpqbCz88PMpkMNjY24HA4aN++PXx9fbFz504MGjQI48aNg8lkwsiRIxEWFsYQ0AcPHmDixIkgDcFxiSMibkWAdhPkX8sx2zQbHlEeiIuLg4+PD+N/WlBQwFhTubq6QqvVMib9RMSQ98zMTBgMBixevBjBwcFvdJsA6kR2crkcUVFRqK6uxtKlS0FEaNu2bZOJU69i7dq1cHBwgEajwbJly1BbW4sVK1agW7duUCqV+PXXX2EymfDixQvcuXMH58+fx6FDh7B161YsX74ccXFxCAsLQ3p6OpKSkhAcHAwnJyfI5XLmXNffuNDrKWevzq+STxOB+hMouu4mssF0m0AOBFrW9Pc4De8mduXl5Zg9ezbUajX69ev3VqHj3wH1SV2vhkGYYYYZZvydYCasfxGncfovezjWT1wDFydrTgKoIwALFixgTPClUim4XC6jrK9XUo8ePRqFhYWorq6GTqfDmjVrsGTJEgwePBgtW7ZkLLECAgIwduxYrFu3Dj/++GOjqEZnZ2dcuXIFK1asYCqRz58/x/Hjx6HRaFBUVPTW933jxg1kZGRAqVQiKyurgaH5gQMHoFKpIJFIGKKUmJiI2NhY+Pr64sSJE+DxeLCwsIBEIgGXywWLxWKM8uuJFlBnCbRt2zamSlnvvyoSiaDX6yEUCpGUlISDBw/CaDSiqqoKqampICKMHDkSdnZ2EIvFkMvliIyMhFgsxjfffIMpU6YgISEBKpWKCVT46KOPIBQKYWVlhePHj2PatGlMRW7+/PnQ6XRMQIJMJoNMJkP79u2ZdK3g4OAG1dBLly5BpVLB0tKSsfRq1qwZU9msrzYLBAIolUo4Ojri+vXrUKlUePz4MYA60jtgwAD4+/uje/fukLSRwOYHG7AqWY08gHk1PLCqWOhs6owxW8bAysoKI0eOBJ/Ph62tLdhsNsRiMdLS0mBpaQmhUIisrCzw+XxoNBocO3aMec0OHTrg448/fus1sGnTJkilUlhZWUEmk0GhUDTyDTWZTCgrK8Pt27dx7tw5HDx4EMuXL4dYLMYHH3wAvV4PlUoFKysruLi4QK1WM5ZYAoEAGo0G9vb2cHBwgK2tLaysrBinBQ6Hw4xMEBHjdmBjY8Pc0NVXxumlU0L9jUVISAiEk4R/OEYMIlAogZ6/9i29RyBnAs1t+jssMAkwB3Peep5exdOnTzFhwgQolUoMGzYMv//++5/e97+NIUOGNEhoM8MMM8z4O8FMWP8FLMXSv0xahSYhmi9sjoiICNy9e5d5rq1bt6J169YYOHAgE4eqUChga2sLFxcXxrtTIBDA3t4e8fHxDfLK66t6CoUCkydPRs+ePeHp6QmBQABPT0+kpqZixowZiIyMhJeXF1gsFsaNG4fIyEhMnToVALBx40Y4ODg0+WN69epVpKWlQaVSYdKkSY1CAg4ePMh4i3K5XIwYMQLPnj0DUDc8+t577zWqdMnlcqxbt67B81y4cAEjR46ERqOBXq9nIlJFIhHs7e1ha2uLGTNmNMiGv3nzJhOwYGFhgWbNmkGpVGLo0KGQSCQQCoVgsViIjo7GRx99hB07duD+/fsNXrd169bo3bs3uFwu2rRpg1atWiE4OBizZ8+GSCSCm5sbNBoNDh48iMLCQojFYnC5XMTHxyM0NBR6vR6TJk1Cfn4+rK2todfrsXz5cjg7OyM3NxenTp2Cn58fWCwWY9MVHh4OHo8HHo8HvV6P3r17w2g0ory8HCtXroSvry+kUimsp1hDaBKCZXx7RZ8FFkQmEVosawEiglqthqWlJQQCATgcDgYNGoTw8HAEBQXBzc0NFy9exM6dO6HT6TB69GhUVFSguLgYOp0Ohw8fZq6rZ8+e4datWzh79iz279+P3r17M0lpRITg4GB06tQJrVq1goeHB6ysrJgYVTs7O/j6+iIqKgrt2rWDQCBAbGws2rdvDzc3N8a7VSaTMdcFj8djqtIymQyOjo4IDQ2Fn58fvLy8kJGRgY4dO6JFixZMdbz+eXg8HlxdXTFlyhQcOXIEX3/9NVJSUiCTyRgPV64Nt66a+raWnikv14lfm+unSkLn9zvj8OHDf8nkv7i4mElGGz9+PJ48efKn9/1v4erVq9BqtY18cc0wwwwz/g4wE9Z/EUuxFCKT6E+Z03MNXCzFUhiNRuTk5ECr1WL37t0AgB9//BHe3t4AgA0bNsDPzw+RkZFQKBTw9/eHWq1G8+Z1RNfa2prxlLSxscHAgQOxf/9+lJeXo3Pnzg3SdqqqqnD+/HmsW7cOAwcOZIZLxWIxWrVqhb59+0Iul+PTTz/F06dPMXnyZKY/EwCKiorQs2dPqDxUiM+PRw9DD3RER7yH9zDTOBObD25GWFgYE/Xp4+PD+H2Wl5djzJgxTNX41YrXggULEBoaivj4eBQVFWHx4sXw8/ODWq1GixYtIJfLmeoqm81Gp06dsHfv3gaesM+fP0dmZia4XC7zvup7LNlsNvr06cNUTrt27frGz/Dp06eIiYkBn89Ht27dEBMTg+zsbISHh0MkEiE3NxdisRihoaEwmUxMBVar1WLs2LEwGo24ePEi+vTpAxaLBSsrKwQFBcHf358x2LexsYGjoyPmz58Pk8mEtWvXQqfTMdVjNpsNR0dHWFpaQiwWIyEhAd9++y0WVy8Gp5Lz9mvr9ekFQThSyLRZ2NvbQ6vVom/fvhCLxYiOjkZeXh42b96MJUuWICsrC25ubpDJZIiIiICnpyeTSsblciGRSGBvbw93d3dIpVJoNBp4enoyfb46nQ7jxo3DhAkTMGrUKPTr1w9t27aFt7c3tFot47kqFArB4XCYwAKlUgk2mw17e3vw+Xz4+vrC09MThYWFuHTpEjZs2IBBgwYhNDQUWq2WuX4EAgEcHR0RHx+P7OxsHD58GKWlpYiIiMCAAQOQm5uL6Oho5mbA0tISer2eCRLQ6XQQfyt+93f2LTcGHas6YuHChfDw8ECzZs0wb948pjr+Z3D79m2kp6dDrVZj5syZDVKz/g5ISEjA6tWrm1xXXl6Oc+fOYePGjZgxYwZGjRoFHx8fTJkyBQsWLMC+fftw69atv0UgghlmmPH/Ht5GWM0uAe/AmktraMTvI6gsoox4PB4Z2IY/VlYSCQQCqtldQ6oVKireW8ysOn78OPXu3Zu6detG2dnZZGdnR+Xl5XT79m2KiIig+/fv0/Xr12n16tW0Zs0aUqlUpFar6fLly1RRUUHBwcFkMpmosLCQ2Gw2VVdXk7OzM5WUlNDBgwepRYsWjKp7y5YtlJaWRi4uLvTs2TPatGkTERFdvHiRDhw4QAcOHCALCwtSq9VUW1tLfD6fVCoVXbe8Tvp/6umm+806yyeqavDeiEVE3xDZb7Qn9S01HTt2jM6cOUNTpkyhgoICEgqFJJVK6dmzZ2QwGGjUqFF07949KioqokGDBtGSJUvo2rVrZG1tTRUVFeTh4UEuLi506NAhKi4uJhaLRUVFRYwX6ZMnT2jVqlW0ceNGunjxIgFtglnzAAAgAElEQVQgqVRKNTU1pNPpKDc3l1xdXcnLy4tKSkoYj9kZM2Yw9l31AEA7duygDz/8kNq1a0c7duygc+fOUa9evSg4OJhWrlxJSqWSnJycqFWrVnT06FESCAT0/fff02effUY9e/ak5ORksrOzo/Hjx1NCQgKlp6fTggULiMViUWVlJVlYWNCgQYPIYDDQgwcPaMeOHcxn0q9fP+LxeLRp0yaqqKggIiK5XE46nY5+//13is+Op92jdlMVu6rBcdNtIhpKRCeJiE9EKVRnu8R9ZZtyImknKZlOm6iiooKx35LJZOTs7EwqlYpUKhUplUpmvnHjBm3evJmSk5OpvLycHj16ROPGjaPffvuNNmzYQGfOnKFmzZoRALp9+zY9e/aMiIh4PB7V1taSUCgkg8FAXC6X1Go1WVtbk5WVFdnb25NOpyNnZ2c6deoUGQwGWr58Oe3evZsWL15MQ4YMoby8PNqzZw+VlZURm80mk8lEfD6ftFotubm5UVBQELFYLPrll1/oq6++avAZXrhwgXr27EkPHz6k8vJy4nK5ZGlpST4+PnTlyhW6d+8eOTg4kFqtpocPH1L//v1pyeklVPr/sXfmcVGV7/t/ZphhBgaYlRn2fREEREQRVBQRFxQBzYUUc0dFFHfLJXOp1FxL09Lccsks06wsNVs0t9KybLPVzFwzzV3g/ftjnCdH0dT8/L7fbx+u8+KVM+fMc855Zqa5nvu+7ut+5XchdHf2Hb8e7sJdvC/eF8kiWQBi27ZtYv78+WLDhg2iVatWoqioSNSvX9/JUeFW+Prrr8WYMWPE9u3bxejRo0XPnj3v2qHjP4G3335bDB8+XHz66adCoVCIb775RqxcuVKsXr1a/PjjjyIiIkLExMSIsLAwAYidO3dKi7Kvv/5afPXVV+L06dMiIyNDFBQUiNzc3P9TnrRVqEIV/veiyiXgH2D27Nl06tQJY5SRyeWT8XnHB/37egopJGh2EKu2rKKwsBAhBCtXrnR67alTp2QBidls5tdff6WiogKTyeSU9r569Srr168nNzcXvV5PTEwMfn5+mEwmUlNTGTBgAL169SIoKEim0PV6PR06dKB+/foIIejTpw8VFRXMmDGD9PR0p2jl1KlTSU5OZuXKlSQlJaFUKvEc5oniguLvI1HlAnFeUHdxXVkAExUVRVZWFlqtFp1OR5MmTcjMzOTzzz+ntLRUpum1Wi1RUVFS52kymQgICECn0+Hh4UFGRgbz5s2jbdu2WCwWqWX19PQkISGB/fv3s2DBAlxcXDh06BBglxVUr16dY8eOYTQacXV1ZfPmzU7zfujQIXJycoiJieHDDz8E7Nq98ePHs3r1atzc3GRkMC8vz0kj+8ADD8hxLly4QLNmzXB1dWXWrFnUqlWLsLAwXF1dycvLo0ePHnh6eqLValmyZIksaPr0009xc3PDarXi4uJCdna2lArIqOQ6l8rnvoVAPGRPS4vfBCJOIGbdEAUsV9DwREOWLVuGm5sbQghGjx7NiRMn+Oyzz9i4cSOLFi1i0qRJ9O/fnzZt2pCUlIS3t7d0RVAqlTJa7ZgLT09PwsPDpcY6MzOTRYsWUVxcjNlspqioiHbt2hEXF4dWq8VischUv8FgkIWFjlau4pqExM/PjwYNGlC7dm2USiVt2rS5qfBnzZo15Ofnc/nyZTZv3kxJSQk+Pj5S1xoUFMSQIUNo166dlGw0btyYdu3aYbFYmDx5MhMmTJDXHj8n3p4ducvodcqilEpbFTtazkZHRxMbG8vMmTMrdXqoDB9//DHNmjUjNDSUpUuX3ra73P8PVFRUEBMTw+zZs0lNTcXHx4eBAweyY8eOO+6Gdfr0aZYtW0Z2djZeXl6Ulpb+r5RAVKEKVfi/BVElCbh3dOrUiV69epGfnw9ATEwMWVlZgL1VZo8ePejTp49s11hZz/SZM2eiVqt59NFHAWjSpAkbNmyo9Hy//fYbY8eORalUEhUVRY8ePWjfvj1Go5GGDRtSrVo1OnfuTIMGDWQa1aHtHD58OO+88w4NGzaU2lWA999/H6vVioeHB3PmzGHiyYkozt+lC8I5QbWZ1Rg5ciQ6nQ6NRkO/fv1YvXo1np6ehIWFodPp0Ol0xMTE0L9/f6xWKz4+PpjNZiIiIlCr1fj4+MiCGovFgre3Nx4eHjz44IPMmDEDq9XKU089RUVFBVevXkWv19O+fXt5L+vXr6dFixYcOHCAgIAAXF1dpV64rKyMmTNnYjabGT9+vFNR2scff0xISAjNmjWTxVUeHh4sWLCAhg0b4ufnR3Z2NhaLhe3btwN2vZ+vr69T4ZtGo2HQoEGMGzeOrl27otFoiIiIkPZS1xcNaTQaAgMDadq0KQ888ADVq1e3p819XeztZCvbqgnEG9c9HioqbQmsuKhAYVVIHbC4pg+12WwEBgYSFBSEn58fBoMBFxd7m+CEhARycnJIT0+X1zl48GC+++47+bmdPXs2NWvWJDg4mDp16jB16lQSEhIwGo24ubnRqVMnCgsLSU9Pl/PvsAVzEOLmzZtjNpsJCQnBz8+PQYMGyQVHVlYWmZmZslXslStXOHnyJEOGDMFms6HT6bBareh0Ory8vCTJS0tLQ6FQYDabGTJkCEOGDMFoNDJixAi++uorMjMzMZlMuLi4MGLECCoqKqQOXVFxZ/rggvcL5IIpNTWVWbNm3USsKyoq2Lp1Kx07dkSv19OlSxe2b99+R1rX9957j7S0NKpXr87atWvvSh97P3H8+HHq1KmDVqtl2bJl/5hAHz16lF69emGz2Vi4cOH/2H1VoQpV+L+PKsL6DxAZGUnbtm2ZNWsWAN7e3rRqZfdo/PnnnzGbzRQWFvL8889jNBrlvhuRnZ2N1WqlqKiIwYMHOxHKypCfn8+wYcPo2rUrBoOB3NxcRo8eTUxMjKySNplMzJ8/n/79++Pv7y81iBqNBo1GI6NaoaGhzJ49m8jISMa+PhbVTBWilkC4XovmObbLAtFW2O1+hEBsvZm0iloCm81GTk6OtFBSq9WEhITw2GOPsXnzZgYPHozRaCQ0NFQWziiVSry9vaXGUaFQ8MADD/D6669z/vx5hg0bRmBgoCSLAGPHjkWtVjtFbp555hn69OnDBx98QGBgICqVioqKCvbu3UtycjKNGjXim2++cZrLsrIyjh8/jl6vJzY2loSEBFQqFaGhoWg0Gvz9/TEajWRlZREVFSWjjeKaptJRcCaEICMjg+LiYh555BGCg4Np3749Q4cOJT4+HovFQlJSEgqFgoSEBCwWy03eqT/99BO1VtW62WbJsc0TiEJhr3Q/LBDVBeLVSo47L1AOV0orLr1ej5eXl7QOW716Ndu3b+fnn3+Wkd+KigqWLl2KzWajZ8+eREZG4u7uzmeffQbYiYfFYqFFixY0btxYfsYcUWHHAkmv11OnTh2KiopYvnw5R44ckSSlRo0afPjhh+h0Ov7880++/PJLhg0bhslkYvz48Xz88cd4e3uzevVqoqKi0Gq1aDQabDYbLi4uxMbGMnHiRHr16kVgYCC+vr5yPl977TWmTp2KxWKhR48e/PLLL7z55ptYrVa8vb1Rq9UsXrzYab73sIdGpxohLgi05c4ODG64oUVLG9qwhz0AfPLJJ1itVkaPHk2XLl0wGAw0btyY55577iYd6/Hjx5k6dSqRkZHEx8fz9NNP/22UsaKigg0bNpCQkEBKSgpbtmy57fH3G99//z0RERH0798fs9nMd999d9/G3rt3L9WrV2fAgAFVGtcqVKEK94QqwnqPOHXqFJ6enkRFRfHpp58CYLPZaNKkiTymbt26pKens3LlSjZv3oxCoaj0R2j8+PEMHjyYDh06EBQUREZGxm3PvW7dOurVqwfAmTNneO6550hJSZERteTkZFkh3bx5c1544QX27NnDtGnTiI+Pl+RCpVJhs9koKCigefPmiFcE4mWBWCsQfSohrDME4kOB8KmEsJYJLO9bpH2TuFZk5erqisVikU0KlEolMTExlJSUMHr0aNzc3FCpVNKT1VHUdOjQIX755Rfq1atH8+bNOXHihLz/c+fOodFoKC0tlc9dvXqVkpISBg8ezBNPPIFer8doNJKRkYG7uzuNGzemoKCAZs2aUbt2bcLDw2WE0WGb5OnpiYuLi0wd63Q6PD09mTFjBm+88QY7duyguLgYFxcXkpKS8PHxkTKGadOm4e3tzaZNm+jdu7dsENCgQQPmzp3Lp59+Sp8+fahfvz42mw2j0YhWqyU8PJzExESio6Px8fHBZcVtCq2+FIgkgXC5tmh4SNj9RCvZ2p5vyzfffENwcDAGg4FTp06Rl5dHWlqak+QE7EQlKyuLxMRE9uzZIz/fISEhqFQqoqOjZRT5+oitt7c33bp1Y9GiRfz00098+OGHWK1WVq1aVenntnfv3gwaNIiaNWs6Pf/NN9+Qnp6OyWRCp9Oh1WqpVq0a7u7uqNVqwsLCiI2N5ejRo9JxQa1W065dO37++WcWL15MUFAQubm5HDhwgEuXLjF48GBsNhuenp7odDref//9Sq+pY8eOWKtbmcIUCimkFa0opJApTOE4x286fu/evdhsNtasWcPFixd59dVXad++PV5eXmRnZ7N06VLpkgFQXl7Oli1baN++PXq9nm7durFz587bRhrLy8tZsWIF4eHhZGZmsmvXrlsee79w4MAB/Pz8ePbZZwEYMWIEAwcOvK/nOH36NOnp6XTs2LGKtFahClW4a1QR1nvExo0bSU1NxWQyyf/5OlLEDkybNo2goCDWrl0L2COpRqPxpjTbihUraN++PRUVFUyaNAmlUnlTNOh6XLlyBavVysGDBwE7aXUQUYcNU0ZGBgsWLGDp0qXk5+dLbau/vz9z5sxhwYIFmM3mv8hlqM45sjfqBsJ6/eZfCWFFIC4KvGPtafz4+HhcXV1lhC8lJYU2bdqQnp6O0WiUxFar1dKgQQPp42o0GmnevDkeHh54eHjQpUsXFi1axIwZMxg9ejT9+vUjNDRUes9ebyDvSHs79iuVSqKjoxk2bBizZ89m6dKlvPrqq7z11lu8//777Nixg6FDhxIYGMjEiRMRQpCYmGjX8V6Lonbs2JERI0ZQUlJCfn6+jAC7ubnh7++PEELabjk0o9drNB3+sw6y59CJOiQSjgit1I9uUFY+5+UCESQQEwXikkCcFIjWAjGs8veoFa346aefCAwMJDk5ma1bt1JeXs64ceMICAhg9+7dXL16lfHjx6PX68nKyqJFixaEhYWh1Wrl9SgUCrmQcERUL1y4wKFDhzCZTDdVue/fv19+xm7EwoULqVmzJsXFxZw+fZoVK1ZQUFCAwWAgIiICf39/uagZNmwY3333HefPnyc7O1vOqYuLC6NGjaKsrIwNGzYQFxdHWloa27ZtA+zkNykpieTkZLRaLTab7ZZdvCoqKvDy8qK4uPjvvu5O2LdvHzabjdWrV8vnzp49y/Lly8nJycHLy4s2bdrw8ssvS9cNsEepn3zyScLCwqhRowZz5851Irc34sqVK8yfPx9/f3/y8vL44osv7uo67xTnzp0jJibGyR3g0KFDGI3G217fveDixYukpaUxefLk+zpuFapQhX8/qgjrPWL8+PG0atWKvLw8+VxqairR0dHy8c8//4xarZaa1IsXL+Lm5kb37t2dxtq1axdJSUnAXz+ikZGRFBYW3rKL0MCBAxkzZoxMsfr4+JCens5rr73G5cuXefnll2nWrJnsZhUeHs6AAQNo0qQJGo0GNzc3QkJC6NatGxaLBfUj6r9M1e+VsJ4XeI73JCIiAp1OR8eOHVm7di3btm3jlVdeoaSkRHrBqlQqoqKiiIiIwGQySeJ8/Z9SqcRgMJCamkrTpk1p1aoVLVq0QKFQkJiYSEFBAR07dqRDhw60bdsWo9FIXFycJI4eHh74+fk5EWR3d3fMZjOBgYH4+fmhVqtJSEiQ6X21Wi2JvKenJy1btqRnz54kJibKAqKUlBR5DgeBvVGj6jDWdzSDsFgsGI1GTCYTTZs2ZdKkSSxYsIANGzawbds2RowYQXh4OIrlt9BUnrg2L39c99xaYZcFVLIVUsivv/6Kj48PY8eOpaSkhG3btvH444/LAqfr59rLy4vq1avTsWNHnn76afbv38/ly5d58803pdWVQqGgZ8+eMjrYqlWrSi2QfvjhByIiInjsscecIolvvvkmrq6uxMbG4uHhQe3atUlLS8NoNJKSksLUqVN5+umn8fT0pFmzZowbN06e10H+g4ODmT59Ounp6cTExLBu3ToqKiqoqKhg8eLFWCwWsrKycHNzIyEhwanBxY3YvXs3KpXqb5tmVIZPP/0Um83GSy+9dNO+33//nYULF5KVlYVer6dTp068/vrrXL58GbBHUN955x3atm2LwWCgZ8+eMrJdGS5cuMBTTz2Ft7c3hYWF/PDDD3d9vbdD7969K23L2qFDB2bOnHlfzwV26Yu3tze7d+++72NXoQpV+PeiirDeI1q1akXTpk2ZMWOGfK6oqAir1ep0nKenJ1OnTpWPV6xYgUKhkNpAsKdf9Xq9/HHPzMzklVdeoXv37kRHR0vJwfXYu3cver0epVJJy5YtuXLlCiaTiSNHjlBWVsby5cuJjY2lRo0aFBQUYLPZ0Ov1uLm50bFjR/r27UtERISMot2Uir4XwopALBWylaqDGDsidI6qbkfa3UEir/dqdfzX09NTFso4tJHR0dGy+n/48OFMmzaNZ599lsWLF7Nq1SqZwg8PD0elUjF9+nR++eUXTp06xcWLF+X8njt3jhdffBGDwcCECRMICAjAy8tLdh1znNOhwb3eoN4hG3Dsd0gYsrOzCQgIIC8vj4ULFxIVFUVBQQFnz56lvLyc6OhoJk2aRFxcHGVlZXz11Vc888wztGnTBpPJRHR0NN27dyduSdytNayhAvGEQFwViNMCkScQBZUvHHSP6qhVq5aUPDiItLu7u5xDDw8POnbseJOW9nrMnTsXT09PjEYjXl5eJCUl0bRpU3755RfWr19PSkpKpa87evQoCQkJtGnThqFDh1KtWjXpHJCcnIzRaCQ1NZXp06fz888/y9f9+OOPsrGAq6sr3bt3Z86cOSiVStauXUtqaiouLi7UrFnTKcPw4IMPSr9iLy8vWrVq5RTdrAxdunSptFvXneKzzz7Dx8fnlhIIxzw888wz1K9fH7PZTM+ePdm8ebPMshw5coRJkyYREhJCUlIS8+fPv+X7cebMGcaOHYvJZKK4uPi+tFE9ePAgFoul0kjqRx99RFhY2H/EueC5556jRYsW933cKlShCv9eVBHWe0BFRQXe3t6Eh4ezb98++fyaNWtQqVRO9i8BAQG0bt3a6fV16tQhICBA/lAe4xjasVoeuPgArWhF7CexZG3K4jjHefHFF7FYLMydO1cef+HCBVJSUhBCMGDAAMAevapRowaLFi0iMjKS+vXrs2LFCiZMmEBwcDBJSUk89NBDxMbGyip1R6V+jx49MH9kvi+ENeLLCEaPHs0DDzxAQEAAVquVzp07M2fOHCIiInjggQf49ttvOX78OH/++SdlZWX069ePhIQE3N3dyc3NpWXLltSoUYN27drxxx9/sHz5coxGI+np6SgUCqKjo9Hr9fj4+JCVlUXXrl0JDw9HCMGqVauoWbMmarWaQYMGMWjQIPLy8qhVqxYBAQGy+5OjPa1D9+so/nIQO19fX6cqd41Gg4eHB8HBwfK4gIAAqX3VaDQYDAZKS0vZtWsXZ86coVmzZrRs2ZKXXnqJuLg4/P39SU9Px9fXl6CgILp168ayZct488036d69OwaDgbZ92+Ja5lr5vO8TiIYCYRAIs0C0E4ijlRx3QSAsf0VPo6Oj0el0zJs3j5CQEDp37szx48f5/fffad68OY0bN3bSCDtw4sQJTCYTer0eb29vGjZsyJUrV5gwYQLe3t688MILBAQEOC2o/vjjD1566SU6d+6MyWSSncqaN28uGw706tVLOgNc/92JiYlBoVAQFBSEt7c3wcHBfPjhh5JgOyyqfv/9dyZMmIDJZKJXr16EhoZSUFBAeHg4er2eQYMG/a1GsqKiQlby/xPs378fHx8fVqxY8bfHHjp0iKeeeopatWphs9no378/27Zto7y8nPLyct566y3y8vIwGo0UFRWxd+/eSsc5fvw4gwYNkm2S79RCqzIUFxffsiVvRUUFderU4bXXXrvn8W+FixcvYrPZOHDgwH0fuwpVqMK/E1WE9R7w448/ysKZ638YDx06hKurq1PEJTIyEoPBIKuxwU4E1Go1Xed0JZ98tGhRXHROBSsvKdGiJZ98Xv7pZRITE2nbtq3sUW80Ghk6dChdu3bl0qVLxMTEYLFYaNSoEZMnTyY3NxeDwUCHDh3o27cvCQkJ+Pn50aVLFxo0aICXlxcBAQGEhITYK55fN9y3CKtCocDf35+CggJWrFjB4sWLsdlsTJs27aZo1qlTp6Rmct68eRw4cICgoCAuXLhA3759CQ0N5d1332Xr1q2yQGvo0KH07t1b3seNUoLr9Y6O1H5oaCj16tWjdevWeHp6UlhYSEpKCkqlknHjxnHq1Cm2b9+OQqGQBUaOSGqnTp145JFHCAwMlGl+RxHPzp078fLywmKxsG7dOsaMGUNERAQhISF06NBBRrG1Wq10bnBoMxctWkSdOnUIDg7miSee4NixY3z22Wd4bvK8525MolzQ4kILtm7dSmpqqtN8KBQKcnJynCJzZWVljBgxgtDQ0Jsi+Q4pxPDhwwkJCcHLy4uvvvoKsOs44+PjiY6Olk4ZTZo0wcPDg6ysLHr37k1eXh4GgwGTyURMTAxTp06lWrVqjB07FrAvvIYMGSIzBRkZGTLz8MYbb8iWuw6t7/VtgcvLyxk5ciQajQYvLy/c3NzQarVkZWXRv39/hBCUlJQwcuRIFi5cyPbt252I3c6dO9FoNLcsxrobfP755/j6+rJ8+fI7fs23337LhAkTiI2NJSgoiGHDhvHJJ59QUVHB4cOHGT9+PIGBgdSuXZsFCxZw7ty5m8Y4dOgQPXv2xGKxMGnSpEqPuR3Ky8sxGo1OEe4bsWLFir8tAr1XjBw5kkceeeQ/MnYVqlCFfx+qCOs9YNWqVSQnJ5Obm+v0/MmTJ/Hw8CAsLEym2EJDQ0lMTGTjxo1Ox7Z5pw3inLgzH0jcmXVlFmlpabIw6NKlS/z444+4ublhNpvRarV0796d4OBgqlevTl5eHvHx8fj4+NC/f39eeeUVevbsidlsZujQocyfP59WrVrJdLEYdi0yd1XYjelHCkTna/++eu1qLl177C8Qb1/79/VV6ucFLiPtkUqVSuVkd+RITYeGhtK0aVOGDBnCY489hs1mQ6vVEhQUxNixY+nUqRM6nY6AgADMZrOMZjrIo+O/DnmBXq8nMzOT7K7ZGB43UPdgXZRvKFEsV5C6NpXGHRoTEBCAp6cntWvXxmAwkJ+fT0ZGBkqlUuqLv/32W6fU+bPPPiudDnQ6Hd26daOwsBCLxeJUPX348GEMBgPe3t4sXLiQkpISYmJi8PDwwMfHR8owVCoVCxYs4ODBgwwZMgSLxUJ2djYbNmygrKyMM2fOMGDAANzd3XGp62K3CbuHTXlRia2ljdmzZ3Pq1CnZGvXGeQwJCeGJJ56QJGflypVYLBa52Nq1a5eUkaxbt47IyEjmzJlDzZo1OX/+PNu3b2fo0KEYjUaEEKSlpTFs2DDatm2LXq+nUaNGzJkzhyNHjnD16lUeeughbDYbnTp1Ii0tjczMTKn1LS0tdSJbly5dYuTIkfJ93r9/P2q1Wu4/cuQIqampBAQEYDKZJCE3m80MHjyYWbNm0b59e2bOnMmECRMoLCwkOTkZDw8PYmJiGD9+PO3atcPd3f2+pbu/+OILfH19WbZs2V2/9vPPP2fUqFGEhYURGRnJmDFjOHDggCwsy8nJwWg00q9fPycpkQNff/017du3x8fHh9mzZzt5DN8O33zzDUFBQbc95sqVK/j5+VUqS/qn2LBhg5OrShWqUIUq3A5VhLUSXL16lV9++YWDBw+yd+9efvjhB06ePCmjg4MHD6ZOnTpMnz7d6XUXL15Eo9FQVFTEgw8+SEVFBb6+vowdO5YePXrI4xym5XdLREQfe9W6xWIhJycHHx8fqal0d3enTp06VKtWDZvNRr9+/Xjvvfc4fPgwnTt3xt3dnaioKKxWqzTlt1gsjBkzhi+++IJuI7rZCeijlUQrH712FcGV7Pvxuqu8KGjVrRX5+fl2i6ZrEU5H56Trq+Jv9adWq2W0rH79+vTq1YvRo0fj4uKCh4cH27Zto3Xr1qhUKurXr8/mM5vJJx91mRqXS846XIeXZj75vHH8DapXr07jxo0JDAyUmlR/f3+ZilYoFNSvX5+oqChJiGvXrk1paSmzZ8/G39+f0NBQzp8/z7lz53jjjTcICAjAz88PV1dXvLy8GD9+PHv27JFEKC0tTVorqdVq9Ho9Dz30EE888QRdu3YlMTFROhJc/+c2yO2uSat7hTtzmcuuXbto0qSJnOv33nuP3377DVdXV0JDQ2UBk0M7nJSUxPLly2UDhREjRlCrVi1GjRpFcnIyffr0YezYsaxZs4bAwEDc3NyIi4tjyJAhTJw4UXYis1qtTJ06laNHj970nSorK8PDw0MS5qioqJu0n2VlZSxduhR/f3/c3NxITU2levXqTJgwAYVCQVlZGbNmzUKtVuPh4UH16tUxm81YrVZ27NjB5MmTMZvNjBo16ib3ArBHFLdv305xcTEKhQJvb+97Kri6FQ4cOICvry9Lliy5p9dXVFSwe/duBg8ejL+/P/Hx8UyaNInvv/+eQ4cO8eijj+Lv70/dunVZtGjRTfe4d+9eWrRoQUhICIsXL/5bMr5y5Uratm37t9c1adIkunXrdk/3dDs4OtJVoQpVqMKdoIqwYj4a3GwAACAASURBVPehfPLJJ2XHIa1Wi6+vL3q9Xkaj9Ho9JpOJevXqYbPZMJvNTkb2YP/BUSgU/PnnnyQmJjJ48GD0ej2fffYZZrOZK1eusJvdtyarK4W9m5G7QIQJxAfO+zVlGooXFWMymWQLUUfq2mw206dPH9avX8+GDRsYNmwYoaGhMvLUpUsXhgwZIj0/ly1bxrZt22jXrh1ms5nhw4fT/ELzv4343nIrE3Yf10pIqEqlkq05Hf92cXGRhvoajYa2bdvKtp6enp6yMt3RMUkIe9tZhyZzypQpJMxNQJy3tyO93bUpKhS4XHSh5nM1qVWrFq6urrz11lu89tprskGAQ8PqiESqVCrCw8OpW7cu/v7+ssq/sLCQ+vXro9PpCAwMJCwsjK1bt3Lx4kUeeugh8vLyKC8v58KFCyxYsMDJtur6sZVKJVarlcjISLRaLQ0bNpTR6J49e3Ly5Ek0pRrcKtzslla3u79yBcqLStwHu9OvXz9KS0tlJN3x/j/yyCOkpKTwxhtvsHv3blq0aIFarUalUslItqurKxkZGQQGBmIwGBg6dCj16tVDrVbj7u5OkyZNGDt2LHq9XsoxHCn4GjVqUFpaip+fH6+//rr8Tpw+fZpevXo5vXdKpZJ33nnH6Xvz5ptvkpCQQHJyMsHBwUyePJnVq1fTuHFjzGYzGo1G6q/79etHWloaYWFhVKtWzald6uHDhykoKCAoKIg1a9ZUWlC1bds23N3d6dmzJ97e3pSUlNzUhe5e8eWXX+Ln53dbW7o7QXl5OR988AH9+vXD29tbLpB/+ukn1q1bR3Z2NiaTiZKSkpvsrj744APq169PTEwMr7zyyi2LyhYuXHhHRPTEiRMYDAaOHTv2j+7pRjgW+FWoQhWqcCf4ryWsV65cYd68edSpUwdvb2/69u3LihUr2LdvX6XRmYqKCo4ePcqmTZvkD71er6egoMDJnkWn03H27Fl+//13GjRogFKp5OTJk9StW5eNGzeSTz4KKiFY7wi7z+YOYScoh6/93UAKNRs0ssDHEZF0dXWlU6dOxMfH4+HhQWRkJDqdjoYNG7J7927WrFlDYmIiNWrU4KWXXmLNmjXUq1eP0NBQpk+fzjfffMOWLVtoNKzRPaeixTlBXNc4unXrhpeXFy4uLjKl3KdPH3bu3Mm+fftISEjAZDJhNBolidXpdHTv3p2NGzdy/vx5Dh06xPz589HpdLRt21Ye52i/6eLiguFhA4oLd0euFecVaEu1jB8/noyMDGJiYoiLi5NEskWLFoSGhjJmzBgMBgOZmZlOGlmFQkFAQACtWrWib9++mM1m9u/fz969e1myZAk9evTAw8NDFmtdT9hr165NQEAA6enpWK1WJk6cSJs2bYiLi2PgwIFy/FGjRgGwefNmateuTUxhDH47/NBUaHDDzel+VFdUTt2Y1q1bh81mQ6PRULNmTRYtWoSrqytffvklffr0kVZPjoKnEydOMHLkSLy8vFCpVPj5+Tml2IWwdy6LiYlh4cKF5Ofn4+npSXJyMgaDga+//hqwR0YDAwPZt28fW7duJSQkhFatWlGnTh35nj344IM0aNAAgOTkZPR6PXv37mXXrl00atSI6Oholi9fTu3atRk5ciQAe/bsISkpiY4dOyKEwN/fn/Xr1xMQEEBUVBQZGRm37B61detWqlevTlZWltTdOtC7d280Gg3nz5/nxIkTtGvXjgYNGvyj4qXr8dVXX+Hv78+iRYvuy3hXr17l7bffplu3brL48Nlnn+WTTz5h9OjR+Pr6Uq9ePZYuXSqdERyLgMTERJKTk3nnnXduIq53SlgBevXqxfjx4+/L/ThQRVirUIUq3A3+Kwnrpk2biI2NpUmTJmzcuNGpqv/vsG/fPvz9/cnJyeHYsWPMmjULX19funbtym+//YbVapUp0fPnz+Mwlu/cuTMFAwvQoq2cTqUKxII7oF0X7Ob8arUao9EouzVFRkYyduxYQkJCaN68Odu3b2f69OkEBgYSGBhI8+bNiY+PR6PR4OrqKlPUN0ZDFX0Vd01aXa+4MvbIWIqKiiSRLy4upqSkRFbEm81mVCoV6enpHDp0iIMHD8rORo5onSNNHRsby8iRI5k4caKMUD788MMcPXrUntJdXGz3jH1aVN5GFoHYLBDRAuEmEI0E4qdrz58XuDVwIz09HbPZLL1VS0tLCQkJITs7m9atW6PRaGRVu8ViwWw2s379ekaOHCmJ2I1epg7drkNn7Onpic1mkwuaY8eO4e/vz+DBg1GpVCQnJ9OhQwf52qeeekp+znr06IG7u7ssVDvOcaduTJm/ZhIyJ4Tj2N0WSktLsdlsvPjii1y5coV169bRsmVLhBD06tWLzz77jC1btkh/2O7du/PNN99w7tw5XnnlFTIyMpx0wrGxsU72XeHh4TzzzDOy8GnIkCHk5uZKEvTYY49RVFTEtGnT8PHxkYupxx9/HIBHH31UVqNPmzaNBg0ayMr/5557jj///JOMjAyKiorkmHv27CEgIEDO88MPP4zJZCI8PJwuXbpIX9Nb4cqVK8yYMQOz2cywYcM4e/YsZWVlsqWqA+Xl5QwaNIjY2Finwq5/gq+//hp/f38WLlx4X8Zz4NKlS6xbt46CggL0ej3NmjXj+eefZ9myZTRr1gyz2UxpaSlffvklYL+3VatWERkZSUZGBjt27JBjrVu3jmbNmt3ReT///HN8fHzuWB97J/juu+/w9/e/b+NVoQpV+Hfjv4qwOn6YQkNDWbt27T35L86bN4/o6GimTZsmnztz5gzDhg2TNlHff/89YCesWq2WDz74gNjYWBTDFaiuqG6mfGUCoRZ2j81wYS9qKhaV+3GeFxgfN/LQQw8xYMAAunTpIlP/jmr064mUI9XrKHBJTk6mc+fO5OXlERERgbe3N6WlpdSsWZNq1aqh1WrRDdGhuqz6e3lAmUB5wZ6KHjhwIBaLhZUrV7J//35GjRpFREQE4eHhxMfH4+LiIs+v1+sJDQ0lOTmZhg0bAvaI0MGDB5k5cyYNGjRAo9E4RTYLCwvlQiCffPu1vSIqbyN7QiC8BGK1sOtyhwpEyl/XrHhVIT1BHcRKCIFGoyEpKUk2JLjRG9ZRPOXi4oLVaqVLly6MGjWKpk2b4u7uTmxsLA0aNCA4OBghBFqtlsDAQKZMmcJbb73FwYMHadGiBSqVipdffhmdTif9RufPny8/o4899hhubm4y0lgZzp49i06nY+3atQQHB9OlS5dKram8vLwYPnw4AQEBpKSkYDQaWbVqFS1btpQLhOTkZKZPn86LL76Ih4cHNpvNiYg7FjlCCGJjY3n22Wel7GXevHn89ttv5Obmynvp2LEjv/76Kxs3biQgIIDi4mIyMjJ4/fXXOXLkCLm5ubi4uNCjRw8sFguvvvoqubm5dOjQQeouz5w5Q+PGjVEqlfTu3VtKYPz8/Hj00Ufv6rv722+/0aVLF/z9/RkzZgx6vb5SIjlgwADy8vLu2Zf1RnzzzTcEBATw/PPP35fxbsS5c+dYtWoVeXl5eHl5kZuby+zZsxk6dKhsJLJ8+XIuXbrE1atXef7556XN3v79+/ntt9/uyoe2SZMmLF269L5d/4oVK2jTps19G68KVajCvxv/NYT10qVLdOjQgfT09FumEe8E3bt3x9fXl08++eSmfZs2bcLFxUUWY508eRKDwQDY06ae6zwrJ36/XiMHtQTiyDXClSYQj9yCKC6xkyEvLy8nc34fHx8UCgXh4eFMmTKFzp07YzQaKS4u5uDBg3z55ZcMHDgQs9lMdnY2a9euZd26dQQFBUmv0eeff54rV66whz20oQ3qMjXKS87tQtVX1GjRkvxzMsYsoyQ4DRo0cGqDeerUKRo1aoSPjw8BAQEEBweTkJCATqeTHaA8PT0ZMGCA9Jw8c+YMJSUl2Gw2UlJScHFxkdZQQgg8wjxQXr6hfemNFlzzhT1i7djOCYRWIL669vg6n1JPT0/UarUsjHL4rl5P2JRKJTqdjg4dOpCVlUVcXBxTpkwhNTUVf39/xo8f72QVdfbsWVlIVVJSwsCBA0lOTkapVDrZNDnGjoqK4vTp05w6dYrs7Gzq169PRETEbSuzjx49islkws/Pj02bNt3yOLPZzLFjx9i5cycFBQXyvJGRkYwbN45HHnlE+qwqlUoSExNZunQphw4dokaNGk5Fc7GxsaSnp0tJTGRkpFwc+fr6Ur16dUm8HTh9+jSdO3dGoVDQoUMHqbt0d3fn4sWL7Nq1C61WS0JCgoyY7ty5k7CwMIKDg7HZbDLirVQqGTx48N99RW+Jbdu2yS5mldlZXbp0iaSkJJ599tl7PseN+PbbbwkICLhpXu43/vjjDxYvXiz9btu3b8/w4cPJzMzE29ubIUOG8PXXX3Px4kWmT5+O1WqlU6dOBAUF3XHHqQ0bNpCUlHTfCH23bt2cFv5VqEIVqnA7/NcQ1qKiInJycv5xcUW1atXw9PS8ZQVubGwsRqORjz76iMOHD+Pr6/vXvu9iKyegv18jR4uve26NQCTegrCu/yvq5yCsjlSul5eX1HsGBgZSv359EhMTZXFTXFwcTZs2pUaNGri7u0vbJq1WS58+fXjyySeZNm0aTz/9NPPmzaNxh8ZEzItA94qO5CPJ1P22LprRGp5Y8ARbt24lICAAvV7PuHHjcHNzw8PDg7p16zJs2DACAwPp27cv58+fp7y8nF27dslCNIflVc2aNSkoKCA4OFgWt3Xq1ElaGTnSyFevXmXMmDGoHlE5t5CtjLAOuBZ1vX6rfm1OEfbXDxFOkWh3d3cZxXOY3Ts0yBkZGWg0Gtq3by/T/o5oa1BQEI0aNaJ79+5MnDiRFStWMHDgQGJiYqhduzbh4eEMGTIEq9XK6tWree+99yQhVqvVsmpfpVLh5uZGSkoKixcvRqPRVPpZraioYOHChXh7e5OcnCw9TW/EhQsXeP3113Fzc8NmsxEZGcngwYN5/PHHqVWrFnl5eTIaX6NGDVJTU3F1dSU9PZ2NGzdSUVFBtWrVMJlMvP7665K8KhQKkpOTZXTY8Zefn8/cuXOpXbu203VcunSJYcOGoVQq0Wq19OrVi4sXL5KUlMT27dsZMGAASUlJBAQEMGXKFJ588km8vb1ZtmwZarUaq9VKo0aNUKlU9O7dm9jY2LuS8FwPhxzA0ZigtLSUP/74w+mYTz/9FD8/v7+VG9wNDh48SGBgIPPmzbtvY94Ox48f59lnn6Vhw4YYjUbatm1L+/bt5VyuXLmS48eP89hjj+Hu7k5kZCS//vrr345bXl5OZGQk63euZzKT6UQnWtGKTnRiMpM5zq3b4N6Io0ePYjAYbts6twpVqEIVrsd/BWFdvXo14eHhlbYfvBucPXsWjUZDdnb2LY9p2LAhEyZMICQkhE8++YSwsDC5L+/PvMoJKAIRYI+cyu2V2xDWJUKaqjsIw/V97B3RqBvtotzd3WWrUYvFgq+vr4yWJSQkkJWVRfPmzWnZsiU5OTm0bt0aq9UqSXjr1q3Jzs6mRo0auLq64unpKcmnQyvrkCBcf12Oa3Fcg0KhQK1WS9J6ferdYdPlkDE0a9aMNm3a0KFDBwoLCwnbHnbzfNxIWLsLxIgbjkkTiEV/PVavtKeXS0pKZPOHiooKunbtSkpKCvPnz5d2TdfPs6+vL7169WLRokVs27aNTz/9lM2bN/Pcc88xcuRIHnjgAXm8g9Sq1Wp69OhBnz59ZARXoVCwbt06wN6m0kH2e/XqJfWd7u7u1KpVi65duzJ9+nQWLVpEvXr1qFWrFnv37mXt2rVOGsQjR47w3HPPkZOTg6enJ+np6RgMBrZs2cKJEyd47rnnyMzMRAhBTk4Oy5cvZ82aNWRnZ6NQKMjNzWXChAlUr16dmJgYzGYzNWvWZMOGDXz//fc0btzYSabRokUL9u7dS0xMjCxOUyqV5PXOY/SZ0aT9kIbbZjf06/VUX1ydL098Sdu2bYmJiaFt27Y0a9aMxMRETp8+zZ49e6T/7k8//UT37t0RQpCVlUVwcDA1a9bkvffeIyMj454joO+++y4Wi4WpU6dy/PhxevbsKW2orm8A0qRJk3u2proVvvvuO4KCgpg7d+59HffvcPjwYWbMmEFKSgoWi4WsrCySkpKwWq0MHz6czZs3y2zN8OHDOXny5C3H2s1uEr5LQHlZeZMW/3oLud38fcR2yJAh9O7d+37eahWqUIV/Of71hPXChQtYrVZ27dr1j8faunUrPj4+TsUxN8JhBt+nTx86depE9erV5b7JTL6po5XcxghEskAcE/aIa32BGH3zcS6XXIh5IUZ6iDoKhzQaDe7u7oSHh+Pj4yMlAo7WoQqFQpJADw8PLBaLNPiPiIggPT2djIwMMjIyaNiwIfXq1SM5ORmVSoXRaJTE08PDA6vVKolp48aNadCgAe7u7nTt2pWQkBCUSiWdO3fm6aefpnPnzvj5+REREcHDDz/MvHnziIqKwmAw8MEHHzBmzBh0Oh0hISHSpissLExWqBuNRnJycpgyZQqLFy8m/qf4vyesAwSi7w3HxF0XYUUg1gni4uIYO3YszzzzDC+99BI9evQgOjqagwcPMmzYMKpVqyYJttFopGbNmqxZs4aJEyfSqVMnkpKS0Ol0+Pn50bhxY4qLi+nSpQtWq5VatWphMBgYMWIEKSkp+Pv7O1laCSHw8fGRzQVKSkooKSnBaDTSo0cPunfvztmzZ9mxYwdz586lbt26MgprtVrJzMykqKgIjUZDr169SEpKkp3NXnzxRU6dOsWxY8cwm82kpaXh5eVFu3bteOmll8jMzGTNmjXyc9m/f386d+7MxIkTJTlMSEjAx8cHg8EgFyAhISE8//zzvPPOO3h6esrPVWRkJCaTicfefAzDuwa75OKGKLj6ql1Gkl+Rz4SNE6Qu9tChQ7zxxhv4+PgwbNgwkpOTSU1NRalUYrFYSEpK4siRI2RlZbFx40b27t2LzWa7p8Vn79698fT0dJKt7Nq1i+TkZNLS0mSb5dWrV/9H+tx///33BAcHM2fOnPs+9p2e//HHHychIUF+Ro1GI9HR0QQGBtK1a1fMZjMTJkzgzz//dHqtwzv6ThudzOXWxHzLli34+vry22+//advuQpVqMK/CP96wjpv3jxycnLuy1hPPvkkZrOZPXv23PKYBx54gJdeeolffvkFLy8vEhMT5b5jHEN1tZKiKwTiyjWSpRcIm0CUCHvB0I3bRUFO9xwefPBBDAYDXbt25c0336S4uFimtZOTk/H19ZX6Q5VKRVRUFOPHj2fNmjUsW7YMvV6Pn58fHTp0YPbs2YwePZrevXuTm5tLamoq4eHhMu1rNpvR6/W0aNGCxo0bo9VqadasGSEhITRq1IgpU6aQlJSEq6srtWrVon379tJbNDs7m3r16hEWFnZTGtnR/SomJoaGDRvSsmVLUlNTZbRYq9USHR1NZGQkrq6uduK0rJI5qUzDmnbd43PC7hbw1XXPLRVSS2swGGR7UIPBgEqlkhFhhz5Xq9Xi5+fHI488wvTp01m2bBkbN25kz5497Nixg/Xr1zN9+nQZaXQsFFJSUggJCUEI+/kMBgObN28mPj6esLAwGjZsyNNPP83DDz9Mhw4dqFatmpyb+Ph4GjRogMViISYmhhdeeIF9+/axcOFCsrOzpYuBg1RGRkbSsmVLWrVqRVxcHB4eHnh4eDBr1iynTlKzZs2Sdkb79u3D29tbRtbKy8tZs2aNk45XqVSi1+t55plnpG3Szz//TFJSEk2aNKF27dp2CcY58bctZRUVClyvuuIx1G7/5e3tjY+PD++99x6XL1/moYcekh3H0tLSJHHKycmRPe27du1624K0ynD16lUMBgORkZE37SsvL+e5557DarXSr18/vvjiC8xm833Tal6PH374geDgYJ5++un7Pvbd4MCBA4wdO5bw8HC8vb1xc3OTi5+cnBxsNhszZ87k0qVL99To5Fak1eEM8Pbbb/8P3HUVqlCF/8v41xPWhIQEtmzZcl/GatmyJe7u7rfV0HXp0kX6LzZp0sRJEgDQ+I/G994nvkzg+Y4nZrOZhx9+mCVLltCqVStMJhPdu3enf//+KJVKgoKCePTRR/n+++85efIkb731FklJSdhsNurXry87HOn1elq3bs3w4cN54YUX+Oijj5xsfTZt2kT9+vWZPXs2mZmZ9OvXD09PT3r27ElpaSlt2rRBo9HIKK+DlMXExJCYmIirqyt5eXmMHz+eefPmMW/ePFncpFQqMRqN9O/fn1dffZXnn3+egQMHUqNGDSeJw42yBuVI5V/Ru1u1kT0u7C4Ba649N1z85RKAQFwQeDzmweeff86BAwd44okn0Gq1WK1W2Zfex8cHnU4n9cEmkwkXFxdatGhBTk4O2dnZZGRkUKNGDdnpyiEFcHd3p0WLFrRv3162LnUQWQdJFkIQERHB6NGjb/L/jY2Nxdvbm/j4eAwGA23atKFRo0Yysq1QKKTjQlRUFI0aNaJz585Uq1ZNFkfVqlVLSjTi4+Pp2bMns2bNYuvWrezevRsfHx/KysqoV6+e1Fbu37+fxo0bS9lC7969GTduHP7+/ri4uBAVFYXNZuPJJ5/kzJkzXLhwgU6dOuEzzsfeie0uNvUVNYq+CuLj4zGZTIwaNYp69eqRmZkpZSuNGjXi7NmzALRv356VK1cC9jS3yWRyahjwd3jnnXfw9fWVmujKcOrUKfr27Sv9g+9m/LvBjz/+SEhICLNmzfqPjH83qKio4JNPPmHAgAHS09nNzY3ExERq1aqFtaUV16uu9rbM3YXdK9pDIGoIxJvXvaPnhX3Bbb723WtgJ617+Gtx//HHH+Pr6/v/TctbhSpU4d+FfzVh/f333/Hw8LjnIo0bYTKZyMjIuO0xffv2lSm/YcOGYbPZ5L6tW7eiqa/5R+b8itr29LTVaiU5OZmioiKysrIwGAx0796dpk2bVlp5W1FRwVNPPYVKpSIwMJCcnBz279/PK6+8wqRJkygsLKR27dp4eXlhtVpJT0+nbt261KtXjwEDBhAeHk5UVBQHDx6UY/755580bdoUFxcXNm3axJ49e7BYLGzevJl3331XpvsLCwvp1asXfn5+Tt2erjfXvzHymp6ezqBBg1iyZAnz588nKSnJrtv1V/8VeX705tfKNrKbhN2HVSsQDcVNLWSFxZ6Sd3RgckRRHYQ1OTmZBg0aoNVqMRqNaLVa2Q40NjYWX19fXF1dMRqNxMTE4Ovri4uLCy4uLkyYMIHp06dLp4Do6Gi0Wi01atS4qZuWp6cnBoNBknjHaxykzUGac3JyWLRoESdOnKCsrIydO3fSv39/bDYbSqWS4OBgIiMjMZvNuLu7ExcXR+vWrTGZTDz44IP069ePDh06kJqaKhs7REREYLPZ6Nq1K76+vgghqFatGnXr1nUiFeXl5QwdOhQ3Nzfc3NwICwtDr9dTWlpKq8daIU79DZl5Xtjt2nQC0UzYXTGufZ6T+ySzaNEiufBRq9XExsZSs2ZNioqKSE5O5vjx404LQbD7uhYUFNzxd7dnz55YrVZ27tz5t8d+8sknaLVa4uPj77iC/m7hIK0zZsz4j4x/Lzh//jyNGzfG29sbnU6Hh4cHyteU9gX2uWvfrR+FvbHJ69fea8f3qpNAdLi2WCwTiI/t8oA2tOHy5ctMmzYNi8XC2rVr/6dvswpVqML/UfyrCevbb79Nenr6fRnr119/RavVMnny5NseN2TIEKZMmQLAnDlzcHNzA2D48OEoFAry8/Np804bVJduIQ24VUTqspoaz9aQaX9Hqr9+/fq8+OKLMjq3efNmJxmCA2VlZeTm5hIWFoZWq6Vr16637Oh15MgR3n33XVJSUmjYsCE6nQ4XFxfc3NyIj4+nXbt2FBUV4evrS+vWrZk4cSI1a9bkwoULTJgwgSZNmnD06FG2bNlCt27dUKvVTr6qDpuk6OhoWrVqxcCBA+nYsaNMv0dERNCiRQsyMzOJi4vDarU6mfKLV8Q/ilKLNX+5LKjVambMmEF5eTmfffYZRqORt956ix07dlCnTh1yc3MZPXo0QghJYouKiigoKCA7O5tq1apJS7DKyLejQMliseDh4YG7u7uMxj7xxBM89dRTTJkyhccee4z8/Hwpm3CQ1cqK5xxk1t/fn0aNGqHVapk9ezarVq1iy5YtfPTRR2zevJmXX36ZgIAAWeAUGRmJRqPB19dXRsUd8+C4rqioKNRqNaNGjWL9+vX8+OOPMjX+8ssvYzabKSgo+MuNYq0CcfY2ZGarQHgLxBcCcVnYpQPpf70XyleV8vyO+xs0aJC0Txo9ejTR0dF06tTJqWDp3Llz+Pn53REBvXLlCgaDAavV6lRcdTuEh4fz5JNP4uPjQ69evSr1uP2n+Omnn2S3uf8tKC8vZ9GiRfj6+tKofaObLO2ctnj7d0l8JRCeAnGmkv9vlakJrxtO8+bNb+o4VoUqVKEKd4N/NWFdsmQJnTt3vi9jrV27Fg8Pj7+NuIwZM4Zx48YBsHTpUhQKBUlJSbi4uLBgwQLArv9zH+KOe4U7ivK/N+cX5wQ+43zIycmRkc7s7Gzc3NzQarUMHjxYFqGUl5cTGBjIZ5995nRdpaWlREREEBUVxY8//igLwm7sQ349YmNjCQ0NpW7dugwcOJBz586xd+9eioqKcHNzo06dOsTFxUnHAq1Wi8FgkJX/jgIjx+OAgAA8PDzYt2/fTfrAixcvysKmMWPGEBAQQO3atRk3bhxt27bF29ubMWPGsGvXLp56/ylcLrncG2E9J9DU+4tcms1moqKi+OSTT8jMzJSLDYfP6pUrV6ioqMDV1ZXOnTvLqvwLFy5QWlqKv78/b731lpSLrFq1Sjo1DBgwALPZTJcuXfDy8iI8PJxatWpJUuqwerrR7UGlUsliL0dHMoce2dPTLgkxGo3SDcLR/jQgIICgR3GozAAAIABJREFUoCD8/f0xmUyyyUFAQABpaWnk5ubSsmVLp8YA1atXp2nTptSsWROLxYJKpUKr1RIeHk5wcDB6vV6miLt160ZGRgZKpRIfHx/GPjP2Zk/cG8nMEIHod93zDs/h7649viCISY+RZNUR6XZxcZHFUTNmzMDDw4Phw4c7fWZeeOEF0tLS/lZr+tZbbxEUFERRUdFtj3Pg6tWr6HQ6/vjjD/744w8GDhyIt7c3c+fOvaWd3b3i559/Jiws7H+dH+mZM2dovLFx5c1LEIijAqG5RlaXCHtRY6mwSwKuK3BUXlTS9UDX/4geuApVqMJ/F25HWFXiXwCFQnFfxtm6dau4evWqqFmz5m2P0+l04vfffxdCCHHgwAEBiMOHD4uvvvpKREZGCiGECAoKEqEbQ8Wlw5fEkYeOiIuNLtrpg/t1131JIRAIxVsKweOIqz9eFQHtA8SoUaNEnTp1hEKhEMePHxfFxcVi3rx54oUXXhBPPPGE6NmzpygsLBRLliwR06ZNE0II8cwzz4iXX35ZVFRUiI8++kiEhISIZcuWicWLF4tGjRqJyZMni27dujnN1QcffCC+/PJLMXXqVHHhwgVx6dIl8e2334rS0lKxf/9+ERoaKr799ltx9uxZoVQqRUVFhSgvLxdeXl6iWrVq4ttvv5VjxcTEiEOHDomjR4+K6OhooVarxZUrV4RGo5HHOM6zZMkSER8fLwoKCkRxcbF4/PHHhVKpFPn5+aJJkyaidu3aoo6ijtCUacTAiwNFhVvFnb+J54Wovqi6iPWLFa9rXxeXLl0Sp06dEqdOnRK1atUSKpVKlJWVibffflt89NFHori4WCxevFhYrVYRGRkp3n//ffG76ndR8kuJWPbpMuHeyV3Ue6KeeO/8e2LTp5tEau1U0bFjR+Hj4yMGDx4snnnmGTFo0CAxc+ZMsXDhQtG2bVvx1Vdfifz8fHH06FFx/vx5UaNGDXH48GGhUChEZmam2L59u/jzzz/F2bNnhUKhEJcuXRJWq1W4urqK33//XXTu3FmEhIQIrVYrysrKxPHjx8VLL70kVCqVcHFxEadPnxZnz54Vly9fFkqlUpSXl4vDhw+LI0eOiIoK+1y5uLjIKTl58uT/Y++7o6I6v2jPFKYwTIGZYWDoVVBQOiJYEASkGAuiEVFs2DXE3oIasaJi71FjQbGL3dhi7JWoWGNXVBQL0pnZ7w/kxhFQU95b6/0y+667gLn9fne4+zvfOXvT8+fPqbS0lIqKikij0RCfz6eioiIyMDAgmUxGpaWldOPGDbp8+TKxWCzi8Xj04sULmvhgIpGmhvv8nIhuEVE9IjpFlc92Fap+v0pEDpV/3218l05PP03+/v505MgRmjhxIh09epScnZ3JycmJ5s2bR02bNqWFCxdS27Ztyd/fn4iIunTpQnPmzKEtW7ZQbGxsrc2+ceNG4nA49M0333zVY3L16lWytrYmqVRKRETp6enUo0cPGjBgAC1dupQWLFhAjRo1qrwcgHn2/+48d+5c6t27N929e5c6duz4t/bxT8+hpvlur7tEwhpuUDkRxRNRVyJyIaKtH9qzHRE9/dDmUURUl0jrqiVNXQ2x6N/5P6yHHnroURNYlYS2Zvj4+OD8+fP/D0/nr2PDhg20adMm2rJlyz/el7u7OwkEAjp37txn15s3bx7dvHmTTE1NKSUlhVgsFpWVlRGXW8n/r127RmPGjKFDhw6RgYEBaTQaKjQsJGEfIckayyif8qn8RTnxb/KJVhN5WnrS8ePHaeHChdS3b98aj3n8+HHq1q0b5efnk0KhoOHDh9PYsWPp8ePHtG/fPuratSsRER06dIg8PDx0ts3JyaG4uDhq0KABLV68mIyMjGjevHk0ZswYIiLy9PSkixcvUmFhIbONQqGgOnXqkI+PDzVu3Jg8PDzI1taWbt26RU2aNKHY2Fhas2YNmZiY0JkzZ2jnzp2UkpJCxcXFVFxcTGq1mp49e0ZWVlbk4uJCtra2tGTJEmrYsCGlpaXR/Pnzae/evfTdd9/RwIEDqbS0lNauXUsrVqygsrIy6t69O928eZMy5ZlUmlpKGq6GiEO1Q0NEJUSqGSoqSS8hf39/EolEdPnyZfr2229p1qxZVFJSQmw2mywtLamgoIDatm1Lpqam9OLFC3r+/Dmd0Z6hvB55RC2peueiuLJzQXuJ1D+rKco0inbv3k3e3t50+/ZtGjFiBGVnZ9POnTupsLCQ3r17RytXrqTRo0fT06dPadq0aRQREUHbtm2jSZMmEYfDobi4OIqMjCRLS0t6/Pgx3b9/n3bt2kW//fYbWVhY0PPnz4nH45GdnR0RERUXF1P//v3Jzs6O7OzsyNzcnC5fvkytW7emgoICIiISi8VUXFxMAMjMzIzKysrI0dGRPD09SSQSUV5eHmVkZFBISAi9efOGXr9+TXl5efTq1SvSarVU9f+AzWZX9mpXgyjhk3tdTpX3yIGIlhDRL0TUkYgOE5ETEX1HRMuIaB0Rffvh/q1lkf8CfwoNDWW+E5mZmVRRUUH379+niooKYrFYJBKJqLy8nEJCQkitVjNk/NSpU9SyZcvKpv6EdJWXl9ORI0eIiKhhw4YE4ItELT8/n8rLy0kikVRbVlZWRqWlpUznDgCxWCzicDj/aNZoNHTz5k0yMzMja2vrv7w9m83+x+fw6ZzePJ2yrbN121dLRJ2I6B0R7SAiAyKaTUQjiKiIiAlzxBBRKBENJoqmaMqirM98QfXQQw89vgwWi3UBgE+NC2sLveL/k5SAnJwc2NnZ/eP9aDQa8Hi8Wl2FPsaiRYuYau6AgACo1WoAlUUWXbt2ZaR1qiSJFAoFVCoVU9CjUqng6OgICwsLPHv2DACgUqlqdT6qQllZGdLS0iAWi2FsbAypVIoRI0bA2NgYxsbGNdp3vnnzBseOHcOYMWNgZWVVrRDKwMAA3t7esLCwAJ/Px8SJEz/rAHT8+HFYWFjA0NAQ2dnZiIiIQEpKCjp06IDk5GTY2dkhIyMDZmZmuHTpEq5fv45t27bBz8+PUQ1gsVgwNDREQEAAevbsibS0NOzatQt37txBeXk5Tp8+jSZNmjCV8oZNDGG03wicMk51B6wiArecC852Djx7ecLQ0JDJNW3VqhUmTJgAZ2dnhIeHw9/fX2dofurUqcx1LcTCykrpL+XNVhBYRSxw+nN09Fa5XC5TJCeTyRjhdkdHR4jFYpiamkIul6N79+4wNDRk2r0m7NmzB0qlElu2bMGLFy9w5swZzJo1C1KpFL1790ZYWBjUarWOGYODgwNGjhyJZcuWYdSoUXB0dMTWrVvRpk0b2NjYwNvbmzGAMDY2RpMmTdCsWTOYm5tDIpGgRYsWiI2NZVQRrK2tYWJiAvbuT9IBNFRZeNOSKmXaqqb5BHIkkCmBJlNlFfmvfy4X/iJktF6tra2RlJSEbt26QaFQ4Mcff0RISAiTs13VPm5ubkhPT8fKlSvh4eGBTp06Ycm2JYjPjkeTB03gk+uDZo+aocWBFlC5qdCkSRMcPXoUx48fx8mTJ3HmzBmcP38ely5dwu+//45r167hxo0buHbtGszMzLB79248fvwYubm5ePHiBV69eoU3b96goKAAz58/x/fffw+5XI7Zs2f/a0Wdjx8/hpOTE6ZMmfKv7O+vQKPR4Pr161izZg0GDx6MwMBAcNZ/knajJVAigZqRbqrALwQyoEqFjqophkDplb8nIOH/+fXooYce/3ug/+UcVo1GA6lUquPz/ndw7do18Hi8L5oPXL58mXGTunLlCkJCQuDp6YmBAwdCLBbDwsICMpkMYrEYdevWBZ/PB5fLhVwuh0KhwHfffYdLly6htLSUyaEDKvNP+Xw+WrVq9cVzffz4Mdq1a8fIMQkEAsyZMweXL19Geno6YmNjGUH8qop1IyMjxtmIzWZjwIAB+O677zB+/Hj07t0bUqkUY8aMqfWY+fn5SEpKgoWFBbZs2YJBgwYhOjoajx49glwuh0QiQXx8PEMC165dC7VajRs3buDo0aNMzuf48eORn5+P3NxcHDlyBIsWLcLgwYMREREBW1tbCAQC2Nvbg8vlgs/nQyqVMkSLY8YBaxgL9sftwcpiweaYDTqc7wCPFh4Qi8UICwtDSkoKTE1NYWdnx0hM8Xg88Pl83L59G2PGjIFYLGaIq6mpKcY+HgteGe8v58maTzDHkiVL8PDhQzx79gyXL19Gy5YtYWJiwriECQQCRjKqyjqXxWLBzs4O/v7+iImJQc+ePTF69Gikp6dj/fr12LVrFxYvXgyFQoEhQ4bg0KFD2LZtG8RiMZo2bcoUL5mZmSE4OBgmJiawsrKChYUFI6316WxoaMjo0EokEvB4PB1HLvqoUK7K5rV+/fqQZcm+TGY+nW4SyJAqzTE+TE6nnKDVarF582a4uroytsLW1tZYv349gErFDRMTE0RERDDOXPShQC9hbgJ4u3jga/nVHJg4JRywSljwfuD9VQ5MixYt+qISSBVycnKYwsCjR49+1TZfwuPHj+Hs7IzU1NR/ZX81QaPR4NatW1i/fj2+//57NG3aFGKxGPb29oiLi8O4ceMwYMAAWM+z1m3L3lQpD1fwSZuWUaUKxMQPpPU3qiy6u17pgDUd0/+vXYseeujx38H/NGEFgO7du2PixIl/aZvneK7jle1zwwfsEWw8La+d+KalpYHNZqNevXqIjIxEfn4+Q0gVCgUkEgkEAgGcnZ0hFAphaWnJFNOsWrUKZWVlOvsLDg7Grl27AADr1q1DUFAQWCwWjh07Vus5aDQa3L17F4sWLdKRa6qKtFUV08TExGDKlCk4deoUCgoK8OTJE/j4+KBz5864evUqvLy8IJfLYW1tjbi4OMTFxdVoVanVarFhwwaYm5ujX79+DMEuLS1Fo0aNkJqainbt2kEqlUIikeD58+fMtjNmzGAKirhcro5cVm24c+cOlEolFAoFY2ZQkzSWubk5duzYgUePHjFC+FXRucTERNy+fRszZsyARCLRKQpjs9mYPXs2duzYAUtLS7D92aA0AnkTiEe65gSg2uWa8KcGpVarxalTp2BjYwMiAo/HQ2RkJFJTU7F69WosXLgQffv2haGhIRo1agQLCwuEhYXBx8cHTk5OMDMzg0QiYUg0m83WIZJVxLLq2uVyOfz8/BAaGoqoqCiYmZnB19cX0dHRsLKyYggsn8+HkZERDAwMYGlpCQsLC6YDw+PxwOVyYW1tDR8fHwQFBcHd3Z2R0BIKhZXnM5T+jGrXRmaKCXTlA6F9QJUSY6M+Wl5EEP4gxM6dOxnVijt37iAmJoa5rri4OMyePZsxDFCpVJg/fz7UajVMU0y/2rDgSw5MOTk5UCgUuHbt2hefxY+/A5s2bYKVlRW+/fZbPHny5Ku3rQ1PnjxBnTp1MGnSpH+8L61Wi7t37yIzMxPDhw9H8+bNIZVKYW1tjbZt22Ly5Mk4cOAAbt26hcWLFyM4OBgymQwJCQlYs38NDCoMKu/g/Q/fL/6H571qXvvhDl8lUMMPnRFXAm2t/FwAAV7gxT++Dj300EOP/3nCevXqVahUKsah53M4i7NogzYQfJh0XnhFrBq9ssvLyxEaGgo2m40pU6Zg9+7dcHR0ZKq8q4S4q2SNxGIxPD09sWDBAqxbtw4cDqcaWQWA8ePHM1XRN27cgK2tLVq0aAG5XA6NRoOXL19i+/btGDhwIAIDA6FSqZgXfBX54vP5cHR0hEgkgrOzM2xsbLBx40adit0LFy7A0tISqampzOfr168HEUEmk+HEiRNo164dMjMzdc7v3r17aNmyJdzc3HDy5Mlq5//48WOYmZnB2NgYdevWZRyGHjx4gKSkJJiYmCAwMBBEhIEDB36xbUpKSuDv78+I43O5XAwdOhQdOnTAlClTGJKlUChgbW3N2JhWyWex2Wy4urqidevWMDY2RlRUFFxcXCAWi5GVlcXIRVVFb728vCo1KDcRaBtVyjF9TFiPUO1yTSAdyaaq2cTEBB4eHmjatCmio6PRqVMn9O7dG8OGDUP79u0hEAgQFhaGTZs2Yd++fTh58iSuXLmC27dv48qVKzh16hSysrKwbNkyxMXFMRHTqrauavcq2ayazBf4fD5UKhU8PDwQHx8Pf39/Jmrt7OyMcePGoXfv3ggICIChoSHMzc3RoEED1K1bFyYmJjodBENbw0pC+jky85oqFQMMqdLBbaQuueRpeOCac9G4cWMYGRmhZcuWWLBgAe7fv4+SkhKEhoYy16FQKHD69GmcOnUKzs7OaLCoAVhFX1DZ+GSqjbTm5uaibt26WLp06RefxZrw/v17jBkzBnK5HNOnT/9s6szX4OnTp3BxcflLnW2tVouHDx9i69atGD16NMLCwmBiYgILCwt88803+PHHH7Fnzx6m4/j69WusXLkS4eHhkEql6NChA7Zu3Yri4mLk5OQgLCwMRgeMvqxkUlsn4YMOqx566KHHv4H/ecIKAB07dvwiKWK8svH1XtnXr19noqenTp3C0qVLGeH3KiJRNcwql8sxfPhw5OTkMMecPHkyzMzMsG/fvmrnU6WDWlRUhMOHD4PH4yEoKIjZb1VkTaVSITAwEAMHDsTWrVsRHx8Pc3NztG7dGjt27EBAQADu3r2LmJgYWFpawsHBAQEBATh9+jS2bt0KhUKBTZs2AagkhQMGDICNjQ3EYjG2bdsGU1NT1KlTBzt37gRQSdDT0tIgl8sxefLkGsl2FUaPHg0ejwc7OzvI5XJERUXBxMQEo0aNQl5eHry9vSEUCmFtbY0HDx7Uup+nT58y1q9V11yV51k11F81DP7kyRPcvn0b+/btQ8+ePaFQKJhhdi6XCwcHB8hksmpEjsfj6VrHKkl3OPRT+9cvyTWBKsmckhAUFMTIjlWhoqICz58/R3Z2Nvbv34/Vq1dDLpdDJpMhLi4OzZs3Z0iigYEB1Go1PDw8YGtry0TmnZ2dkZycDCsrK4Zsd+vWDTNmzEBWVhauXbuGmJgYpKWloWHDhhgwYADS0tLQpUsXNGjQQCdSW3UPxGIxo/NrZGQEoVDIEGGhUIiGDRuie/fuaNCgQSUZOmPx9wmNhgX/x/5wd3fH4cOH8fr1a2zcuBEJCQlQKBRwc3PDiBEj0KFDB8jlcojFYrBYLFhYWGDQmkHgltagZSz6ZGYTaEB10vqxA9OtW7dgb2+P8ePH/2P5pVu3bqFly5ZwcXGpMW/8ryA3Nxeurq6MTN6nePLkCXbu3IkffvgBkZGRMDU1hampKaKiopCSkoKsrKxq6VDv3r3DunXrEBMTA4lEgjZt2mDDhg2Mde/r168xePBgKBQKpKenY9rhadVzw79y+vQ+66GHHnr8E/wnCGt+fj5sbGxqdVn5O17ZvDIeWH1Z8PPzw88//wyVSqVTtFT1kvf09MSBAwdq1G9s3rw5evTogR49eqCiogLZ2dmYPn06WrduDQcHB53hfC6XCycnJ4a0HjlypNrLderUqVAqlWjYsCGKiopQVlYGlUqFmzdvAgB27twJGxsb+Pr6MmSkiojevXsXPj4+aN26NbZt28YYLjx48AASiQS+vr7Yv38/PD09ERIS8lVD+P7+/mjSpAk4HA7EYjHEYjGz3e7du2FoaIjZs2dj4sSJsLa2xr59+3D06FFkZWVh7dq1GDJkCEOsqjRF2Ww25HI57OzsmPzPj4vElEol7O3t4eHhgYYNGzIRVicnJ3A4HBgbG6Nr164wMTFBnz594OPjwxRHSaXSP8X0h5Hui7omwtr3o78ffyCs2z/6rJDgt8kPI0aMQNeuXREREQEPDw+YmZmBy+Uy9quhoaHo3Lkz41Dl5+eH/fv348qVK8jLy8PFixcZ/VOpVIqhQ4fi0aNH6NevH5RKJZKTk9GxY0ewWCxcuHBBpw26dOmCLl26wMbGBqNHj0Z4eDjEYjGTN1s1OsDj8TB27FgMGzYMdnZ2THRapVKhQ4cOmDx5Mvr06QNra2sYGRnBzc0Nnp6eMI0y/dvObaxCFpQtlYyerLW1NcLDwzFy5EisXr0aP/30E4YOHYoGDRow68THx6NVq1aVhgVfKoIr+EBaj31yXG1l5K+8vBwLFiyAUqnEsmXLvvg8fy20Wi127NgBOzs7tGvX7rOdsS/h2bNnqFu3LoYMGYJdu3ZhwoQJiImJYcwfwsPDMWbMGGzbtg2PHj2qkXAXFhZi06ZNiI2NhUQiQWRkJH7++WedTlRFRQVTMJqUlITnz58jLS0NarUaw+8N/8v/H7+UfqGHHnro8VfxnyCsAHD69GkolUommliFszj7+X/Gt6hyqDO++jJOCQfKlkodN6KqfMgFCxagbdu2TNFIFbRaLe7fv4958+YxJPRjkiuRSFC3bl106NAB9vb2WLFiBTQaDZKTk5mipQYNGsDe3l5nvxs3boRMJoOjoyNevXrFfJ6cnKxTMJWfn4/69euDzWajSZMmMDExQbt27aBQKDB79mzGwvXjiLSXlxdcXFzAZrMxcuRIFBUV4fnz57hz5w4uXryIY8eOISsrC+vXr8fixYsxY8YMdOzYkSne4fF4sLCwgJmZGaRSKWxsbJh7xuPxoFAoYGJiAj6fD29vb6YorCpqWLUei8WCr68v+Hw+WrdujYiICJ37Hh0djbVr16KgoADv37+Hr68vxo0bx1zH7du3IRKJYGJiApFIxKg2VBH5j4uQjHcZ67b2p4T1IFWKpGdTZSQ2iUAsAq3XfUbMD5rj22+/Rf/+/ZGSkoJ58+YhIyMDBw8exPHjx3H69GmcP38eBw8ehEQiQXZ2Nry9vdGvXz9MmzYNlpaWIKq0d12/fj3KysqwY8cOKJVK9O/fX6etXV1dYWxsjHnz5mHixImIiYmBQCAAi1XZsYqPj4ejoyPc3d1x8OBB5OfnIyMjA3w+n+kUVNm8jh8/Hjk5OdBqtdi7dy8CAwPh5OSElStX6kTVp0yZAupT6cT2l+hqIUE+Vo66desyhhJqtZpx9xIKhTA0NASHw4FUKoWVlRUMDQ0hEAhApgRW8VdEdVcRyI4q82c/nYoJ5vXN0axZs2omG/8WioqKMH78eJiYmGDSpEkoKSn5qu1evnyJ/fv3IzU1FW3atGHyi+3s7DBixAhs2rRJx4GsJpSUlGDHjh3o1KkTpFIpQkNDsXz5crx69QparRYXL17EtGnTkJiYiLp161ZLH6nqxISEhGDYsGHofrY7hFohWNqvH4HSQw899Pg38TnC+v+9DuunuHz5MkVFRdGgQYNoyJAhxOVyqS21pe0fwmI1IoyIionIhojWfrJMQ0TbiSi20jBAJpORs7Mz5eTk0LNnzygyMpJcXV2psLCQLl++THfv3qX8/HxGnJ3FYlFoaChlZ2fTsGHDqE+fPmRgYMDsfsyYMcRms+nHH3+kdevW0Y4dOygzM5OePn1K1tbWNGnSJBo5ciSdPHmSIiIiSCgU0rlz58ja2prZR3Z2NrVq1Ypu3LhBDx48oISEBJJIJBQfH09z586l27dvU3l5ORkYGFBwcDA5OjrS7t27SSaTkUKhoAcPHtD169fJ0NCQ2Gw2vX//nthsNslkMpJKpSQWi0kikTA/ORwOXb9+nbKzs8nS0pJevHhBM2bMoNTUVOrWrRtlZGSQv78/ZWVlUXp6OsXExFBJSQllZ2fTyJEj6caNG8ThcMjGxoYCAgJo586d5OPjQ8eOHaPOnTtTvXr1qKSkhAoKCmjt2rX07NkzEggEJBKJyNbWlh4+fEj5+fnE4XDI0NCQ1Go1lZSUMPPbt29Jo6muds9isUgoFFJFRQWZmprS08VPSRv1kSnBWCJ6TESrPtpoARGlU6Um5XdENJWIdhFR4z9XUV9QU8CUAKqoqKg2l5eXM7+/fv2aXrx4QWZmZoypABERj8cjPp9PAKiiooLKyspIq9USi8UiAwMDYrPZRESk1WqpvLyc0Uqteh5fvnxJRER8Pp9KSkrIwsKC2Gw25efn09u3b4nH4zEatG5ublRaWkpPnz6lyMhIKigooAsXLlBFRQU1atSI3N3dicfjEZfLJS6XSwcPHqTDhw9Tu3btiNWXRZsDNhN4+KwmLgss4ml51PN6T2p8rTG9efOG/vjjD1qyZAl17NiR8vPzKS8vj549e0Z5eXn0+vVrxuigqt0wBEQTSEcLt0Y0J6ImRDS+hmVFRKzxLOLM5lBQUBCNHTuWQkJCvrDDv4d79+5RcnIyXbt2jebMmUORkZHMsjdv3tDFixfp/PnzzPzq1Svy8vIiHx8fZjYyMqLQ0FBq3bo1TZw4sUZDlPLycjp06BBt3LiRduzYQe7u7tShQwdq164dqVQqevbsGS1ZsoQyMjKotLSUmjVrRleuXKFHjx5RamoqJSYmUkFBAbVr1474fD5NmjSJHj58SNevX6cTJ07QscJjJJwopFd+r0jAF1Axq5g5tpCEBAJFUiSNolHkQzVLJeqhhx56/F18Tof1f46wEhHdv3+fevXqRbm5uTR+4XhKaJJAJVRS88obqNLFpS4R3aHqhJWIWKUsavtdWzqw7gC5u7sTEdGpU6cYNycul0vm5ubk7OxMPj4+1KJFC2rUqBGNHz+ehEIhjR8/nmbNmkU5OTm0fPlynX3v2rWLUlNTad26dfT7779Tv379aPny5fTu3Ttat24d7dmzh7p3705r164ljUZDwcHBxOFw6N27d4xbUkFBAeXn5zNi72KxmKysrMjAwID++OMPIiIqKysjhUJBZWVlVFFRQRqNhkJDQ+nq1av04sULqqiooMTERFIoFFRQUECbN29m1uFyuVRSUkJv3ryha9eu0cOHD0mhUFBeXh4pFAoqLi4mmUxG79+/Z8hHlRC8kZER4+ZUUVFBRMSQM19fX8rOziZzc3O6desW1atXj4KDg0kgEBCXy6VNmzbR48ePicvlUnFxMUVHR1NCQgLx+XyaO3cuXblyhYqLi6lHjx6UlJQNEcgpAAAgAElEQVREAoGALl26xLyUORwOyWQyKioqovbt29OTJ0/o4sWL5ODgQM+fPyfVARWddT77Z2PURFg/xi0i8vywjvGfHydQAv1MP3/miaxEr169aPfu3fTs2TNSKBTUoUMH2rBhA23evJmaNm1KBQUFNGnSJFqxYgX5+/vTzZs36dmzZ9SgQQNmJqo0rhg3bhz179+fWrduTT/99BMZGBiQu7s7PXv2jN6/f08SiYRevXpFZmZmJBaLSSgUUnZ2Ni1cuJC0Wi2tWrWKTpw4QSqVisLDw8nV1ZU0Go0O2d63bx9dunSJgoODydzcnPbs2UPiYDGVfl9Kz72fEwssgvDP/x/sEjaBBTI+ZUzmq8xJcFXAEPby8nL6448/yNzcnADoEPmPyT0REZfLJc1qDaFT7f+biIjoARHZU+X31q7mVTqjMzVf1ZzS09PpypUrJBKJKCoqiiZOnEjOzs5fbLO/ii1bttDgwYNJKpWSg4MD3bhxg3Jzc8nDw0OHnDo5OTEdkY+Rl5dHISEhFBMTQ5MmTSIWi0UajYZ+/fVX2rBhA23dupUcHR2pQ4cO1L59e7KwsCAiotLSUpozZw5Nnz6d4uLiKC4ujo4cOUILFiyggQMH0rBhw8jQ0JDu3LlD0dHRFBkZSTNmzNBxRCMievXqFbVp04ZuvLpBkkESco1zJTImMiZjcid3SqREUpLyX79veuihhx5E/+PGAbVBq9Vi27ZtMJliUvvQ4lsCORHoEYFSak4JqBrapCHEFAP5+voyKQHu7u6YN28e9uzZgw0bNmDZsmVIS0vD2LFjoVQqERoaiujoaPj5+YHD4cDBwYERa/9YeN7ExATm5uZgsViwt7eHi4sL6tWrx+TMstlsODo6wsPDA/Xr14ebmxtcXFzg6OgIGxsbRuKqqmipKm1BKBTC3Nwctra2MDExAYfDgampKTMsaGtri7i4OBgaGiIhIQHJyckYNWoUUlJSEBYWBrFYjG7duiEiIgJGRkaIjo7GunXr0KNHD8TExMDCwgIZGRm4d+8ecnNzsWDBAhgbG4PL5cLMzAxKpRIhISEICQmBh4cHLly4AK1Wi5EjR0Imk6Ft27YwNjaGv78/M/xZXFyMiIgIODo6wsfHB8HBweDz+di9ezeAykK2Bg0a4O3bt7h06RLc3NxgZWXFyGG5uLjAx8cHHA4HDRs2xJ07d1BaWoojR46gX79+MDc3r7z+Ksmm8srhYxpJoM4ffq/67HNyTSAItILPalBWVFRg2rRpMDMzY+73wYMHUVRUhFOnTqF///6MFJlAIACXy0V8fDxWrVqFq1evVsuLLi0thZGREc6cOYNGjRox7ahQKODl5QWpVApfX1/MmDEDd+/ehVarhZOTE06fPo2AgACMGDECrq6u8PPzQ6dOndC8efMac6+7d+8ONpuNjIwMHD58GCqVCvPmzcOhQ4dgamqKeRvmYTqmIwEJYGWx0KmiE6Zj+mfljSIiImrNMQcq1T5cXFxQUlKCiPKIL6cD/Ei6qg01TNGIZvZfWFiIlJQUWFtbg8ViQaVSITk5Gfn5+bWe0+fw/v17/Pbbb0hPT0fnzp2ZNBd/f380bNgQIpEIffv2RUFBwV/ab15eHtzd3dG5c2f0798fZmZm8PT0xLRp03Dv3r1q69+9exeurq6Ijo7GzZs3sWHDBlhbW6NDhw46ubXHjh2DSqXCokWLaj12WVkZ5HI5bt++jfnz50OpVGLatGn/uFBNDz300ONrQP+VHNaa0EnTqfbX2SACTf3w++cIKwicdRzweDymep8+uBuxWCwYGBgwMkNVBLRqHSMjI8jlcqjVavD5fKYgqmnTpoiMjERsbCzkcjmio6MxcOBAWFpaonPnzkhNTcXUqVOhVqtBRAgNDcXmzZuxa9cu/PLLL/jtt99w7tw5XLlyBT/88AMUCgWEQiEePXqE4cOHw9LSEsePH692P7Zs2cLIcVVVavfr1w9SqRQvX77UWffFixfo2LEjU1hW9bIsKyuDhYUFFi5ciAYNGjAvM41Gg/Xr1zNkWaVSYezYsYiOjkZ4eLjOi3vKlClQqVSMoH1V7l9JSQkiIyMZjdKpU6ciMjISBgYGePLkCdasWQO1Wo3U1FRERERALBajefPm8PX1RZ06daBQKDBq1CjGUGH48OGIioqCWCyGr68vxowZg2PHjuGHH374UyUghaqL7afQF+WaCJXb9x7bm6nArsKTJ0/Qvn17xrQgIiICxsbGaNeuHRo0aAChUAgvLy8kJSUhISEBVlZWcHd3x4oVK2p8jrVaLbKzs5GSksI4eclkMuZ3IyMjTJ8+Hffv39fZ7sSJE3B2dsby5cuZ53D//v3QarWoqKhAkyZNMHfuXJ3jtG/fHhwOB9u2bcP8+fOhUqlw6NAh/Pzzz1AqlTh06JDOMaRS6VeRvpSUFIwcObLW5Xfv3oWtrS0AIB7xXyasTgRa8fl12hS0qfFYjx49Qs+ePRnnNWdnZ8yaNatWRYyioiKcPn0a8+fPR2JiItzc3CAUCuHr64u+fftixYoVyM7O1tn+wYMHaN++PWxtbbF9+/Yvkj6tVouzZ89iyJAhUKvVEAgECAoKwo0bN2rd5uLFi1Cr1Zg/fz4uXryIxo0bw8PDA7/++qvOeqtXr4ZSqcSBAwc+ew579uxBQEAA8/fjx4/h7u6OgQMH1tix0UMPPfT4N/GfJqzRiK75VXaJQHWpUl/zKwhrvT/qYdWqVdixYwcOHz4MkUiEX3/9FQ4ODti7dy9yc3Px+vVrFBcXQ6PRIDMzE1FRUTrnMnPmTPTo0aPaOQ4dOpTRYvzuu++YiEZsbCzEYjF8fHwgFAqr6cyWl5ejf//+cHV1xR9//IHQ0FDUqVMHYWFhePFCN9JVVFSE0aNHQ6FQIDExEb6+vjAzM0NiYiKSkpJAREzRSF5eHkaMGAETExP069cP2dnZiIqKgr+/P+7du4fMzEw0btwYbdq0waJFi5CXl4dp06bBzs4OCoUCPB4PderUwYgRIyAUCtG0aVOdF/nevXthbm6Onj17gsViwd3dHW/fvkVJSQmio6MRHBwMhUKBq1evYsiQIWjWrBn4fD46deoELpcLmUyGzp07IzMzU8fIQCqVQiQSMYVtUqkUtra2yMzMZMh41X2lD4oDgj2CL1ei1zZpCNwdXHC5XIhEIsyYMQNbtmyBk5MT47JlZWUFgUCAunXrgsvlYt68eThz5kw1C9527dpBJBLpFO0UFxdjz5496Nu3L6ysrCCXy8Hn82FoaMhoyfbv3x9JSUlwdHREVFSUDnEuKSlBUFAQZDIZQkJCsGzZMjg6Ouoc99y5c7C2tkZ5eTm0Wi0iIyPB5XKxa9cu9OrVC/Xq1cOdO3cwceJE2NjY4OrVq9WeX7VajcePH1f7/FPs3bv3sw5TT58+hUKhqOyozVZ/3k3rxIeOxLvPrFNIYA9nQ61Wo2/fvjh9+jQ0Gk214549exbR0dEQCATgcDjw9fVFWloaFi1ahJ49e8LDwwNCoRCenp7o1asXlixZggsXLny1Dusvv/wCV1dXRERE4NatWzrLtFotLl++jFGjRsHe3h5OTk4YO3Ysrly5gpcvX8LDwwNDhw6tkezeu3cPSqUSK1asQK9evaBSqbB06VIdYqnRaDB69GjY29t/lVlCQkIC5syZo/PZ69ev0bhxY0YzWg899NDj/xb+04S11kjNbPozcqaiSmkcAYE8a375uV9yx86dO5GbmwsAMDc3x5MnT2Bvb1+j/FNSUhJmz56t89mDBw8gl8urRXGysrIQEhICAFizZg3i4uIwatQoiMVi9O/fH2VlZTA2NkZ09J/Dm69fv0ZYWBjCw8Px5s0bHDhwADKZDHZ2dtVeyr/88gscHR3Rvn17PH36FMOGDcOkSZOQn5+Pfv36QaVSgYgQHh4OmUwGIyMjJCUl6QwnajQazJw5E0qlEq6urli4cCGMjIzQoUMHyGQyJCYmYtOmTTA0NETbtm1x7949SKVSGBkZwcvLi/Fiv3XrFpRKJcaPHw8ul4uZM2eib9++aNSoESIjIxEZGQkbGxusXbuWkQ3icDgwMDCAUCjE3LlzmX1VDfOPGjUKcrmciWzb2dnhxo0beP/+PdRqNc6fPw+gMjJsb2/PWLJGRkaCG8AFq/DvaYwaag2RkpUCW1tbHRUJAwMDhIWFYfbs2fjtt9/w/v17HD16VCdy9SnCwsLg6emJuLg4LFmyBK1atYJYLEZgYCCaNGnCVNN7eXmhf//+MDc3R4cOHQBURi7Hjh2Lbt26wcfHB3fv3kV6ejrUajW4XC62b98OoJIcqdXqaqSpSZMmyMjIQNOmTWFgYIAdO3YgKCgI33zzDV69eoVu3brB29ubefY/hZOTEyOr9jm8evUKYrG4WqTuwYMHSEtLg6enJ1gsFnr37o0tx7dAoBXUfveTqDJ94zMTX8vHip0rEBUVxVj0GhoaomXLlti8eTPev3+PsrIyXLp0CcuWLUNSUhIcHByYtuRwOHBzc8Pq1aurdTD+KkpLSzFjxgzI5XKMGjUK58+fR0pKClxcXGBjY4Phw4cz6TKf3jNPT08MGTJEZ1lZWRlj7atQKJCcnIzXr1/rbFtYWIjY2FgEBgZW68DWhKKiIshkshrbOS8vD5aWljXqSeuhhx56/Fv4TxPWaZhWzdGqKvpCuR/NQwjUjkAvqr/42CVseKz1gJ+fH0QiEYyMjJj8Ux6Ph/j4eMyaNQt79+7F/fv3odFoYG9vjytXrlQ7n4YNG1b7p//69WsYGRmhtLQUOTk5UCqVEIlEaNWqFUM+Dx48CBaLhSNHjuDOnTtwcXHBwIEDUVJSgpSUFKjVauzbtw9yuZwZFs7Ly2P0ObOyspjjhYWF6fy9e/duJsUhKCgIrq6uaNSoEc6cOVPt/JcvX85IEonFYsycOZORXYqKioKhoSHu3LkDrVYLGxsbmJiYoHHjxvjxxx/x9u1buLq6YuTIkRAIBAgJCYFWq0VxcTGsra0hk8ng5OQEBwcHiMViBAcHw9raGhKJBIaGhli/fj1ycnIwZ84cZpjf29sb9erVA4vFQvfu3eHg4ACVSsUQjPnz56Nly5a4cuUKeDweYwPq7OwMgUCAiRMnYk7pHLCL2H+NrhYSuAO4jKRUVZRVJBKBw+HAzs5OR1R+1qxZ6NevX7X7qdFocOLECXC5XLi7u4PD4cDd3R2DBg1CUFAQOBwOjIyMMGDAAEYgPjs7G0TEEM8ff/wRo0ePxtu3bxnXqNDQUEyePBmhoaE6x+vZs2e1jtS8efOYyPjatWthY2ODsWPHIj8/n8nB/lwepoeHBy5evFjr8o/h5OSEK1eu4PHjx0hPT0dAQADkcjl69uyJXbt2gcfjMeu2QZsvmnzUNn3qwKTRaHDq1Cl06tRJx82LzWZDpVIhNjYWc+fOxcmTJ1FYWIiysjLMnDmTkaQzMTFBr169/pEt6507dzBixAjIZDJwOBxERkbi5MmTX0wVePXqFby9vZGcnMys26tXL4hEIoSFhekYlVQhNzcXvr6+iI+P/2qyvWXLFjRv3rzW5UeOHIFarf7H5F0PPfTQozb8pwnrczyvmbB+OqVQ7SkBH9yMeDwe6tWrhy5dusDY2BhOTk4MaZVIJDA1NYVEImGchI4ePVot2llbWoCHhwdOnjyJAwcOgIjg6elZ7cVQlbOpUqmwYMECPHv2DCEhIQgODmaiIn379sXEiROxevVqpqjkU7JhZmaGhw8f4vXr1/jhhx8Yp6VJkyZBqVRi0KBBWLBgAdRqNeLj4/HgwQPk5ORg4MCB4PP5cHJyAo/Hg7OzMxNZO3HiBIyMjNCnTx8AwIEDB+Dm5oZx48bB398fSqUSTZo0QadOnSCXyyGXy5GXl4czZ87AxcUFEomEuZcrVqxgciIVCgVTrGRpaQkrKyv07NkTmZmZOHDgAJycnKBQKPD9999DoVAgJycHERER+OmnnwD82Rmoir6mpKRALBZDJBIhKysLv/76K1xcXFB/YX1wSjhfTg+oILCL2BB8J2CiqV26dMH79++h0WiQkZEBCwsLZtje29sbFy9eREJCAiNcX1BQgG3btqFHjx4wMzODjY0NFAoFfvjhB7Rs2RJEBLFYDIVCgUWLFuk8Q1XD9paWljh69CiASovfoKAgKJVKdOjQAePHj4dKpUJAQADWrFmj0/Zbt25FixYtmL+Li4vh4OAAFouFtLQ0KBQKbNy4EQ8ePICbmxv69+//xdzFwMDAGvOlP0UViXJycoKxsTESExOxZ88eZsRBq9WCxWIx1/tF/eTPTIZaQ2y6twlr1qzB4MGDERgYyNgXd+rUCePHj8fgwYPh5eXFWNyam5sjKSkJZ8+e1bnnL1++xHfffQeVSgUWiwUbGxtMmDABhYWFX7zmhw8fIi0tDT4+PjA1NUW/fv1w7NgxHDlyBO7u7ggJCamRcH6K/Px8+Pj4oEuXLmjZsiW4XC6jqfwpLl++DGtra0ycOPEvFUu1b9/+i7a1ERERteZZ66GHHnr8U/ynCSvwzyI1VEEQ7xfj+PHjOH/+PIYMGQILCwsIhUL07t0bPB4P79+/x/Xr17FmzRoMGjSIqUKussHs0aMHjh8/jsLCwlrTAgYNGoTvvvsOIpEIfD4fO3bsqHYdixcvBhEhMjKSiXaMGzdOh1Bs2rQJQqEQHh4ezFD4x3j+/DmkUinGjx8PhUKBbt264fjx47CwsABQWWjVrVs3qNVqLFu2DO3atWOihwMGDIBEIsHPP/8MPz8/LFq0CAqFAqtXr4aXlxdEIhFjp/rtt99i0aJFqKioQFhYGJycnCAUCuHg4ACRSISoqChYWFjAyMgIdnZ2GDNmDGxsbNCmTRv4+vpi2LBh8PT0ZIbZR48ejRs3bkCr1aK8vBwTJkyAUqlEYmIiAgIC4OLiglWrVgGoTLFo3LgxMjMzdVIFunXrBqlUCrVajZMnTyIpKQkWFhZYvnw5srKy0HNxTwh3CytzJz/JnxRoBOCUccDexgZ5V0aj27Rpg3nz5qFVq1ZQKBQYPnw4Hjx4gLKyMixduhRyuRw8Hg9cLheGhoZISkpCeHg4jIyMEBoaiqlTp2LWrFnw9vYGl8tF06ZNERAQAJlMBpFIhN9++61a++3cuRN16tRBcnIyRo4cibFjx8LQ0BCurq64fv06s97atWvBYrGqmVq8e/cORkZGKCgoQEFBAZycnGBoaMi4UF28eBEXL16EhYUFZs6c+VWEJywsrNah4hcvXmDx4sUIDg6GTCaDr68vwsLCahXYFwgEOkTw7zjUsYvY4A/mw8HBAXFxcZg+fToOHz7M5Dt/ipKSEuzZswft2rWDVCpl2is8PBybNm3SyR2/fv064uLiYGRkBDabjQYNGmDVqlU69yk3Nxdz585FYGAgTExM0KNHDxw8eJBJZalCeXk55syZA4VCgaFDh+Ldu3e13uO3b99i4MCB4HK5qFOnDho3blzjellZWVAoFNiwYUOt+6oJ7969g0Qi0TGpqAkHDx5E/fr1/9K+9dBDDz2+Fv95wvpPIzU/7PwBarUanTt3xqNHj6DRaODl5YWoqCgQERo2bIg5c+YwUc64uDgsX74c2dnZSE5OhrGxMSQSCfh8Ptzd3aFUKjFgwACcPXuWeXGvWLECPB4PJiYm6NatG6ZP/1MqSaPRYMSIEbC3t8ekSZPAYrGgUCiwf/9+Zp3S0lKkpqbCxMQESqWyWpUwUPnS69q1K7hcLrp06cLk3t68eVOnGOfRo0dITExkCpzGjRuH+Ph4iMViNGzYEC1btsTKlSsBVEZz1Go1eDweRowYwezD3t6eiRylp6czKQdEBLVajalTpyIiIoLJJxSJRAgKCoKRkRGMjY3h6OiIhg0bMjJdVaT85s2b8PPzQ4sWLXDq1CnI5XK0adMGXbt2ZY597NgxsNlsKJVKEBEsLCzA4/FgbGwMPz8/DBkyBGKxGLa2tlCr1TA2NkaLFi0watQobN68GTHdY+CwyAFWR6zgfMMZqv0q0FCCxEGCYcOGoaioCLm5uZg2bRqcnZ3h6uqKUaNGoXfv3jAxMUFsbCyOHj2KQ4cOoXnz5jrKEYGBgUhLS2NyVCMjI5GQkABnZ2eYmJhg3LhxePv2LbZv3w61Wq2TR1xUVAQ7Ozts3LgRbdu2hYGBAXr27Ilx48ZhwIABOm2dlpaGmJgYqNVqzJs3T2dZSEgI1q5dCysrK4jFYoSGhoKI8OzZM+zevRsKhQKbN2/+wrfqT7Ru3Rpbtmxh/n716hWWLVuGFi1aQCqVomPHjti2bRuKi4tx9uzZzxIeY2PjaqSpirR+sdOpIRiUGWBQzqAvEq/aoNVqkZOTg2HDhsHe3h4cDodJ05gyZQqTlgFUkrdmzZoxbm116tSBp6cnZDIZEhISsGvXrq8qzHr27BkSExOhVquxdu1aHfKr0Wjw008/wdzcHN26dcONGzegVCoRFBSks55Wq8Xs2bNhbm6OU6dO/eXrXrt2bbUi0Zqg0WggkUiQl5f3l4+hhx566PEl/OcJK/D3IjUf2w8WFBRgzJgxMDExwfjx49GyZUtkZmaCx+Nhz549SEhIgEwmQ/PmzWFkZITff/+dOXZZWRkmT54MCwsLrFu3Du3atYOzszPq16/PVB9LJBIQETZs2ICffvqJKah5//49WrdujcaNG+PGjRuIiIhgqs+rcOLECdSrVw9RUVG4f/8+UlNT0bt3b2b5u3fvkJqaCqVSCS8vLyQkJOjcm0uXLjFWnm3atIGxsTEGDBiAy5cvIz09HXK5HCNGjICZmRlcXV3B4XCYHNiysjJYWloyygC///47Xr58CZFIhAkTJsDd3R0sFoshjxwOB+vWrUNAQAAsLCxgbm7ORBerqvnLysrg5OQEsVgMU1NTqNVqaLVaLFy4EHK5HPPmzWMit7GxsXBxcUFBQQGeP3+OXr16wdTUlJF7sre3Zyxgq45vaGiIjh07YsOGDUy+7ccoLy+Hr68vQ7Dr1q2LrVu31vhcabVaHD9+HImJiZBIJPD29mZSRYRCIVq1aoXJkyfDyMhIJ28yKioKt27dYpY5OjpWK3ZJS0uDu7s7E3n7/vvv4ejoCGNjY/To0QMikQjl5eVYuHChTntrtVq4u7vj6NGjuHv3LurUqYNhw4Yxw9wTJkyAUCiEVCqFs7Mz0zlZtGgRzMzMcPLkya/4Rv2J+Ph4LFq0CCtXrkTLli0hkUgQGxuLzMzMasPmpaWlMDQ0rDUn1tzcnFEc0Gq1ePjwIbZu3YpuC7rB9LhpZXpOYfXotwACtEVbnMO5v3TuX0J+fj6WL1+OwMBARtbOzMwMPXr0wOHDh7FixQqEhYUx97NKYuzbb7+tsRjzczh58iS8vLzQuHFjZGdn48SJE/D29kZAQADOnj3LrFe/fn3UrVsX/fr1g1arRVlZGfr06QM3N7cadVq/BtHR0dXSR2pD8+bNsWfPnr91HD300EOPz0FPWD/gr0RqWEUs9DhXPdf0/v376NChA4RCIXr06AGZTMYsKyoqwvTp02FkZASJRIJWrVohIyODkRrauHEjlEolduzYwaQFvHv3jhkSFolEsLW1hVAohEAgQLdu3Zhh8kOHDsHKygojRozA48ePweVyMXbsWPTt2xfm5ubYuHEjQ7wePnwIExMT5OXlYcqUKVAqlejUqRNu3LiBhIQELF++nDnn/Px8DBo0CAKBAPXr18fixYurkYknT56gcePG4PF4aNu2LSIjI2Fvb4+oqCiMGzcOcrkcs2bNwvfffw+BQACpVAoOh4PevXvD3Nwcbdu2hbW1NROJIiLI5XLMmDEDzZo1Q//+/XWOt2DBAjg4OKBevXowMDBAUFAQWrZsCW9vb2bYe+XKlXB1dYVcLseFCxeQmpoKqVSKoKAgiMVinYp9Ho8HPz8/sNlsfPvtt7UOR798+RKJiYkQCoUwMDBgit8+5w9/8+ZNzJw5E8HBwRCLxXBzc4OtrS1MTU3h6+sLmUwGIoJEIsGMGTOQlJQEAwMDRqu2QYMGWL9+fY1RR61Wi6SkJAQHB6Njx45gsVjo0aMHU/jj5uaGs2fPYunSpTp50RcvXoSNjQ1DUF++fInAwEB07NgRd+7cgVQqZaL0VTJbCoUCTk5Of4lkvX37FmvWrIG1tTUEAgFat26NjIyMLwrlN2zYEEeOHKn2+ZMnT2BmZob+/fsjMjISpqamUKlUiI6ORkpKCrKysnDx0UUIxgnQrrAdohGNBCR80bDg30JFRQX27t2LgIAApoDvUyWBwsJCjBs3DlZWViAimJmZYciQIdUq+D93jClTpoDP50MkEmHJkiXVOlQymQz37t1DQEAAunfvjtDQUERERODt27d/67pevXoFiUTy2ZSEj9GnTx/Mnz//bx1LDz300ONz0BPWj3AO59AWbSGAAEIIdYiqUCsEX8uHwU4DzD4+G0qlEn/88UeN+6kqfDEwMNAZgps+fTr69++PN2/eYNWqVQgPD4dEIkHHjh2xc+dOZGZmwtbWFj4+Pti3bx969uzJVGf36dMHM2fORH5+PlPI5e7uzlQ1u7m5YejQocjIyGDSERISEqq9DN+/fw9HR0dmOPbjoo4GDRrg7NmzOH/+PLp3785odHp6en42XzE0NBTDhg0Dh8NB06ZNkZOTwwj0czgciEQiNGrUCMnJybCzs4NAIICjoyPjqiUSiRAcHAwzMzMIhUKm8CUoKEgnn3f79u0wNzfHH3/8gXHjxoHFYoHD4WDMmDHMevfv34dMJoNCoYCrqysT+XJxcWGIBI/Hg52dHWQyGVxdXREYGIg5c+bAw8OjWiHcr7/+Cj8/P7BYLJiamuLHH39EeXk5CgsLweFwEB4ezgztlpWV4dChQ0hOToaTkxPUajWSkpKwc+dO3Lt3D0uXLkVYWBhEIhEcHR0hkUgYMiiRSNCsWTNYWlrC1NSUGW6uynX9lHDcvrXtvXIAACAASURBVH0biYmJMDAwgFQqrSa6369fP6SlpWHlypXo0qUL8/ngwYMxbtw4nXWLiooQHh4ODofDFLgtXboUxcXF8PLygqmpaTXjiJpQUFCAjIwMtGnTBhKJBNHR0YiIiEBqauoXt/34/MaOHYvdu3djwoQJiImJgbm5ORQKBSOptn37djx+/LjaM3nw4EH4+fl99bH+DRQWFmLTpk2IjY2FRCJBZGQkfv75Z1y5cgXjxo1DnTp1mLasV68eJk2ahNzcXDx8+BDdu3dnzAnq1KmDOXPmVMtlrUJxcTEmTZoEuVyO5ORkdOvWDWZmZvjpp590nlmZTIb8/HxcvnwZQqEQ9erV+2pN2JqwbNkyxMbGfvX6/fr10xNWPfTQ4/8K9IS1BrzAC0zHdIQ+DYXggADti9ozkZoRI0agd+/eSE9Ph5+fX40vgwEDBjD2qxYWFujUqRMePnyIsLCwavaTL168wIIFCxAUFAQTExO4uroytqM8Hg+TJk0CAGRkZOCbb75BZmYmuFwuRo4ciZiYGDRs2BDZ2dnYv38/hg0bBjMzM8ZlSywWY/To0di6dStu3ryJGTNmQKVSwc/Pr1phxps3b8Dj8eDj4wMbGxtMmTIFz58/R1ZWFiIjI2u9Vzk5OVCpVFi7di28vLwQHh7OyHoZGBigXr16MDY2RufOndG1a1fG+pTFYkEkEqFXr15o3LgxOnXqhGbNmsHV1RUSiQRyuVxnGPz06dNQKBQ4e/Ys3rx5AwcHBxARbGxsEBAQgF69esHLywtsNht8Ph9CoRAqlQrp6ek4ffo04zRmbGwMPp8PqVQKY2NjLF68GBqNBlqtFl5eXti0aRMqKiowdepUpvLb29sbv/zyS7VrDwgIgJ+fH7y8vBAbG8sUDk2YMAEXLlxAbm4uFi1ahJCQEEgkEsTFxSEzM5OJqpeWlsLR0ZFJpTA0NIRMJkNQUBAWLFjAkMiqtIn9+/cjJycH8fHxkMvl+OGHH7BixQoYGBhUk6NavHUx6q6qi0Z3G0F9QY14xGNyxWQoXBXVIqVXr15lUiOqIuVjxoxBYGAgFArFZ4eDqwhb+/btIZFIEBERgZUrVzJKDmPHjmWML2rCy5cvsX//fqSmpqJNmzaMKkVoaChGjhyJzZs34/79+0z7nDtX+7D+gAEDMHny5FqX/1soKSnBjh070KlTJ0ilUoSGhmL58uW15sYWFhZi3bp1CA4OhlAoZOSyEhMTce7cOZw5cwaRkZH/h733jmvq7t//XxmQQcgOIwTClK0yRfaooy6coAKCKG6lLrSOSl1I3QMrWG3FOlpvW6vWWTdaxb23rVpxUlCUIZDr9wfl3CIB0fa+P5/P75tnHjyAk5OTk5MczsX7/XpdFxNO0Lp1a6axUqfTYfPmzbCzs0P37t3r/JOcn58Pf39/BAQE4PTp0wAAhUKBrVu3wsLCAnPnzkVQUBBSUlL0hiI0haioqPeqWR4wYECj8a4GDBgw8KEYBGsjBAcHY8OGDXWWPX36FHK5HL/99hs6deqE8ePH13tcWloaUlNT4e7ujpKSEkydOhUymQzGxsaNpv7cvXsXs2bNYhpxXF1d8euvv0Kn0+GPP/6AQCCARqNBmzZtIJPJMHr0aFRUVKCqqgoLFy6EQqHAjBkzUFFRgYMHD4LFYqFz585wdXVlhFxISAgmTZoEExMTXLx4ETdv3sTYsWMhkUjA5/MxatQojBkzBrGxsfDx8WEakwYOHIi5c+di+/btTA59RUUFOnfuDD8/P/D5fCiVSgwfPhzz588Hm80Gm81mOv/Nzc2hUCiQkJAALpcLkUjEeJN269YNERERePXqFfbs2cOI7VrBevPmTcaua8KECTA1NWXKB5ycnBibpkmTJjFd7bNnz0ZFRQU2bNjATP9bWFgwQrdTp071fDPXrFkDU1PTmpQrPh/x8fH1TNV1Oh0uXLiA2bNnM5G6FhYW8PHxwb1791BQUIBly5YhPDwcEokEffr0webNm/XaHJ07d47pwF+0aBHS09Nha2sLW1tb2NnZQaPRYPTo0XB0dGTKGLhcLoYNG4bi4mKUl5fD0dERq1atgoWFBXbu3Il85KMbuoGn49VzMzCuNAarnIVu6IZ81NQ95ufng8/nQyAQoEOHDpg9ezbzWe3Xrx+TdPUmZWVl+PHHH9GnTx9GsK1cuVLvKGxGRgbTcFdUVIR9+/YhMzMTvXr1gp2dHcRiMSIiIjB+/Hhs3LgR+/fvh1qt1nt+BAUF6W0YrH1frK2tm5TY9CG8fv0aO3fuRFJSEmQyGUJDQxn7uPdBp9Ph5MmTSEpKgoWFBVgsFgQCASIiIrBu3TqsX7+eKVHh8XhQKpVwdHSsF3lbS3V1Nb766iuYm5tjyJAhcHJygkQiwc6dOwHU1KgHBwdjwIAB7y1aHz58CIlEUi9FrzF8fX31OlgYMGDAwN/FIFgbID8/H1qtVu8U3aRJk5CSksIkvNReHGpJT09HcnIy3jxGGzZsgEKhgEajwdq1axu8eJw8eRIcDgdcLhd9+/ZFs2bNoNVq4e7uDmNjYwwfPhympqYIDg4GUFOT6Ovri/Dw8DqJQqWlpWjdujWICF26dMHZs2dx7949bN68GRMmTGCmIlksFuMLampqitjYWMyePRvr1q3Dhg0b0LdvX4SGhmL58uUYNWoUIiIiIJPJIBKJwOPxwGazMWDAAIjFYsybNw8dO3YEl8sFh8NBSEgIU6d6//59ZGVlgcPhgM1mIzQ0FA4ODhCJRFAoFLh79y5KSkrg5ubGGJ97enpi0KBB4PP54PF4kEql4PP5zPMJhUIANUJIq9UywnT//v0AgAkTJoCIwOPx4O/vD5FIBD6fD09PzzrHfNu2bUwDmLGxMeLj4+u8P2VlZdi5cyeGDx8OrVYLW1tbZhS9Xbt2uH37NlxdXaFUKiGRSBAfH48tW7Y0aKJ+9+5dJCUlMZ6zb9bBVldXY+/evejduzdEIhEsLS3BZrOZEena19ihQweMHj0a0dHRAIC8vDyIxonAr+a/sw6bBRaEEGLMjTEwNjaGkZERJkyYgOrqahw7doxpEHJzc8OSJUsA1IwGb9u2DfHx8ZBKpQgPD8eXX36Jx48f632NL168wMGDB9G1a1c4OTnB0dERIpEIISEhGD16NNatW4fr16/XOw90Oh2USiXu379fb5tRUVEN5t2fPn0ajo6O7+Ut+i6qqqqwf/9+DBo0CEqlEgEBAVi4cGGTomabyuPHjzFr1iwmGILD4cDJyQk+Pj4QCoWM17BCocCgQYMaTBQrLCyEn58fiAhhYWF17OxKSkoQEhKC5OTk9xKtS5cuRXx8fJPXLy0thVAobJIHrQEDBgy8LwbB2gDjxo1Denq63vsKCwuhUChw584dHDx4EBYWFnUsbebOnYvY2FhGVAI1Infy5Mk4evQo/Pz84O/vX6/j+t69exCJREzSUnJyMh49eoTmzZvDwcGBEbK1CVVjx46FmZkZvv76a+ZCXVZWhiVLlkCtVqNLly5MXR1QM2IyY8YMaDQamJubM8Jx+PDh8PLygq2tLUxNTeHg4IDY2FjMmzcPn3zyCbp27YrMzEwEBwdDLBYjOjoaY8aMQbNmzZiLrEgkwsCBAzFv3jyYmJggJCQEOp0OpaWlTACBRCKBm5sbfHx8GAHm4+ODxMREKBQKWFtbw9LSkglYqLXO+vjjj+Hq6ooOSR3w2cvPEKeLA20lSLZKMOjmILiFuUGlUoHD4SAiIgLV1dVo3749I+5CQ0PBZrMRGRnJjJqXl5fj008/hVwuB5vNRkhICPLz83HgwAHY29vj7t27WLlyJaKjoyEWixEcHIw5c+bg0qVLTId6bYSsVCpFfHw8WrZsidjY2AbN9AsLCzFu3DjI5XJMnjwZOTk56NWrl9518/LyEBUVBalUCgsLCybgwNHRkfGPJSL06NEDBQUFWI7lMK40fqe7RZ3bSwJrGIuZ8t+0aROUSiV+/vlnWFtbg8PhYPTo0cyoYnBwMJYuXVpPNL18+RJ5eXlYtGgR4uPj4eLiAhMTE7Ru3RqRkZEICQnB5cuX3xkyUEvHjh31TkN36tRJrwcxAHz22WcYO3Zsk7bfGNXV1cjLy8OIESNgYWEBLy8vZGZmfnCH/ftQWlqKAQMGMP9E1MYE9+zZE7GxsUzNt62tLWbMmMH8Q1RaWorY2FhGUPP5fPj4+OD48ePMtktKShAWFoakpCS978NjPEYmMhGHOHRCJ8QhDtplWqzfu77eug2xatUqtG/f/u8fCAMGDBjQg0GwNkBISEid+My3mTJlCtN9nZ6ejsjISOZCkJWVhY4dO9ZJDfL392e6n6urq7F27VpoNBr06dMHd+/exfPnz2FpaQkLCwsUFxfDxsYGpqam0Gq1GDhwIBwcHBAZGYnQ0FAm8Ugul2PGjBkoKChAeXk5srKyYGVlhc6dOzM1bfv37wcRITw8HFKpFNHR0bC1tUW7du2gVquZ2MzaSNaqqiqcP38eEydORMuWLZmpd6lUCi8vL0RGRsLFxYVJhBoyZAj4fD5kMhmmTp2Kdu3aQSKR1IluraiogI+PD/OY+Ph4JrK0VrhaWVmBzWYjLi4ODx8+RFlZGcRicU15QKQpvH/3Bl/Hr5dMxiplgfOaA85PHASmBqJly5bMCKFQKIRWq2VsmY4ePQpLS0umNlQkEmHo0KF4/vw5qqurmQx3U1NTCIVCxMbGYu3atcxU92+//YZ58+ahVatWUCgUSE5Ohrm5Oc6fPw+gRjhERkaiX79+dURBaWkp5syZA6VSicGDBzOlCGPHjq1Tc6nT6bB//35ERETA1tYW2dnZKC8vh06nw6lTpxAaGsocr1pXBRaLBaNAI3AruPUF6RUCRRBITCAHAv1QX7Tyq/jI1+Vj3rx5sLKyQn5+PlJTU5lQAx6Phw4dOjAjnqWlpfj111+xbNkyJCUlwcPDA0KhEP7+/hg2bBhWrVqF8+fPMzMTGzZsQExMzHudezNmzNBbatOzZ0989913eh/TokWLJiVq6UOn0yE/Px9jx46FRqOBu7s7ZsyYUWfG4j/NL7/8And3d0RGRjKxzZcuXcLgwYOh0WiY0gFfX1/4+fnBxMQEbDYb7u7ucHR0RGxsLEpLS6HT6eDn54fU1FRYWloiOTmZKWt5+fIlwsPDkZiYyHw+a0tI+Kh/blEpga/j1ykhaQidTgd3d/dG/2YaMGDAwN/BIFgbwNTUtFGD8T///BMKhQK3bt1CVVUVwsLCmG7or7/+GuHh4ejSpQuzrkgkqmeB9PLlS3z22WeQyWQwMzODiYkJIwyCg4PBZrPRpk0bqFQqfPfdd8jPzwePx4O9vT1cXV2RmZmJ+Ph4CAQC8Hg8eHh4MBeM58+fY9myZXB3d4dIJIJAIMC3337LGL/rdDpMnToVqampAAAzMzMsWbIEvXv3hkwmg6+vL0aMGIHg4GDY2dkxtlpeXl5o1qwZU+cZGhoKV1dX7Ny5E56enjA2NmamqZ88eYIdO3bAz88PIpGIseeqHfkUi8XYvn07Y6A/adIkBAcH46OPPkL37t1rBOtQFlilLLB077Abq6rx3DQaacSI1dr60uvXryMnJ4cZtXJwcEBubi5KSkqwZcsWDBw4EJaWlnB2dsbYsWORlZUFjUaDsrIy3L59G5mZmfD19YVSqcTAgQOxe/duxpWgT58+TNRr7XsaFhaG5ORkVFRU4KuvvoKVlRW6d+9eJ20KACIiIrBz507odDrs3LkTQUFBcHJywjfffFMv7ayWZcuWwcTEBFwu999WYD9Q/djYSgI5EWj+X/ftI5CQQNffKg/QsWB3xg52dnbo27cvc9yGDBkCqVSK7du3w9raGs7Ozow3sLe3NwYNGoScnBycOXOm0S70rVu3Nsl0/k327NmD0NDQessTEhKYxLI3uXPnDlQqVZNHcIEagXXu3Dl8+umnsLe3h5OTE6ZMmcKIxf8Wt2/fRrdu3WBnZ4cffvihwZKG4uJizJ8/H97e3kxTo7m5OdOUV9uoduTIEfz0009wcHDAvXv3MHr0aMairNbdIjIyEgkJCVhWvaxJVn61JSS1vtP6WLx4MVq1avWPlmQYMGDAwJsYBGsDGBsbN+qxCdRMQyYlJQGoSYAyNzdHXl4evv/+e/j7+zMG/z/88APatWundxs6nY4RbCqVCt988w0WLFgAPp8PMzMzyOVyXLt2DdnZ2VCpVBCLxTh79iyGDh2Knj17wsbGBm3btkVGRgZ69uwJExMTaLVappnpwIEDePXqFYyNjSEQCOqYjO/duxcikQgBAQEgIrRt2xYpKSno3bs3NBoNbGxs0LJlS8TExNRrqGnfvj0mT54MOzs7REZGws3NjZmm5vF4kMvlMDU1hbOzM1OneuzYMdjZ2cHCwgKpqakQCoWwsbGBmZkZsrKyEBQUhObNmzM+lQHfBIBXxXvvaW7hmJrGK61Wi0GDBjHT6RqNBnv37kVWVhbat28PkUiEyMhILFiwADdu3GBe240bN+Di4gIrKyuYmZlh8ODB+OWXX/TWMy9ZsgQpKSl1lr148QKurq6QSqUIDQ3Vmy6k0+kglUqxZs0a+Pr6wt3dHevXr3+n6KqoqIBarYZEIkHfvn3RJ7VPjWH+27eLBDIhkO6NZW0INEXPuqUEra8WKpUKtra26NevH1O24ejoiISEBLi5uSEoKOi9U4z27duHiIiI93pMUVERRCJRveOdkpKCFStW1Ft/0aJF6N+/f5O2feXKFUybNg0uLi7QarVIS0vDmTNn/utCq6SkBJMmTYJCocCsWbMarHfWR3V1NaZPn86c07WBBLUzC2KxGI6OjoiOjoZOp8OlS5cQHh7OjEK/evUKzgudwSnnvNe51ZBoPXPmDJRKJW7duvVPHiIDBgwYqINBsDaAsbHxOy8iRUVFUCgUjE3Qtm3bYGNjgw0bNsDT05MRs8OGDcPcuXP1biM1NRUcDgc7d+7E4cOHmSQmgUCAVq1aQSqVIjAwEAEBAbhw4QL69u2LhIQEKJVKJnmovLwc69atQ1BQENRqNbp3746IiAiIxWLExsbiyy+/ZETbN998g3HjxsHZ2RmWlpaQyWRwc3NjAg06d+6MpUuX4tq1a9DpdBgzZky9fT979iwkEglGjRoFgUAAR0dH8Pl8GBkZwc7ODpaWlnBycmKEJ5fLRVxcHNzc3MDn85kayBMnTjAjhTt37sTjx49hb29fU6fXyxb0J4GSCWRDIBGBWhBox1+Xz2//EmS1X4IasUynCPSKoGyvZMSzWq2GnZ0d3N3doVQq0a9fP3z//fd18uOvXr2KGTNmoHnz5rCwsECvXr0gl8vfabh+8uRJuLu7M7/n5eUhMDAQbm5ucHFxwdChQ+uJoerqaixbtgxcLhctW7bE5s2bm9wMM2fOHHTs2BGFhYUYPnw4hOlCcCr0CA99gvUjAnXVI0VeEWhcjRtEQkIClixZgmPHjmHMmDGYMmUKgJou+f79+8PX1/e9OuOPHz/+Qd6oLi4uOHfuXJ1lo0aNqmffBdSMVG/ZsqXBbd26dQuzZs1C8+bNoVar8cknn+D48eP/I6OBteVAVlZWiI+P/6AGrqVLl8LCwgJHjx4FUPP6Ro4cCVtbW8abuHY2QSgUYty4cSgqKsLGjRuh0WjQfmp78Kvfmv4vb+Rce0u0vpkYdvr0aSacxIABAwb+kxgEawNotdo6o24NkZ6eXseY/ZNPPkFwcDAcHR0xZMgQAECzZs1w9uzZeo/NysoCm83GihUr8OeffyIyMhKenp7M9LmNjQ04HA6GDx+OsrIyrFq1ihGqa9asgZ2dHSZOnAgzMzO0adMGP/zwQ51RqadPn2LevHmMv2Ptxaxt27YICgqCSCSCra0tVCoVevTooXdqd+DAgRg7diyWLFmCfv36wdXVFVwuF5aWlvD09ER0dDTOnDkDKysrmJqa4sGDB3j48CHMzMwglUohlUoxe/ZseHt7g8ViwdLSEhKJBOHh4fDw8GAac2p9W/l8Po4fPw6PGx6g5wSaRqDfCFRNoG1/XUx/0yO6viaQ/V8CrYrA/7nGDYCIwOfzkZaWhqNHj9YZwbx06RLS09Ph4eEBtVqNkSNH4vDhw8w6PXv2xJw5cxp9/1+/fg0TExMcP34cXbp0gY2NDdasWYOqqioUFxfD398fqamp0Ol0qKysxNq1a+Hq6sp0gr+PaLp//z5ThlJL5+ed9Y+HvSaQHYEy//p5N4GMCNRW/+hZz9L65vCHDh2Ct7c387tOp8O0adNgb2/f5PrOixcv1hH0TSUxMRHZ2dl1lqWlpSEjI6POstokprc70+/du4d58+bB19cXZmZmGDZsGA4dOvTBfqT/BPn5+QgICICvr+97R9wCNbHAw4cPh5ubG+7cuaN3ndLSUixduhT+/v5M/Xntl4ODA+bOnQvHC471S0heNu1cY4GF7ugOoOYfdJVKhc2bN/+t42LAgAEDTcEgWBugV69eTcrPLi4uhlKpZC7g5eXlcHZ2hlQqxejRo3H37l0olcp6F8pdu3aBw+Fg/PjxuHHjBmMg7+7ujtWrV4PFYjFuATY2NrC3t0dERATWrl0LpVKJDh06gIgwfPjweuLh9u3bWLRoESIjI8Hj8cDn8+Ht7c2MsnK5XLRv3x47d+7En3/+CSMjIyxevBgVFRU4deoUVqxYgQEDBqBFixbgcDjM1HpOTg6OHj0KuVyOy5cvQyaT4d69e5g/fz6sra0xadIkADVOBTY2NjA2NkZWVhZGjBgBkUjEGMg/ffoUw4YNg7m5Odq1a8fUtRIR2Gw2OiV3Aud1A9OVngT6l57l4QRKf+P3MoKpvSlTtrB48WLGQ3Xq1KlwdXWFRqPBJ598gry8PL1C5sqVK1CpVHVGYt/m/v37sLCwgEQiwbx58+qMyldWVuL+/fvw9PREaGgotFotvL29sWTJEiQkJKBPnz746aefsHHjRnzzzTdYsWIFFi5ciIyMDEybNg3jx4/HkCFD0LdvX3Tq1AkqlQpqtRru7u6wt7eHWq2G0S4j/ccJBDpPoFACyf8SqnF/jaLpuXVCp3qvrbKyEjKZrI4DBgDG97N2hK8x7ty5A1tb23eu9zbLly9HcnJynWXTpk3DZ599VmdZbm4uUzNdUFCAJUuWIDAwEHK5HAMHDsTevXsbTI/6b/Hw4UMkJSXB0tISX3/99QeJ5ufPn6N9+/Zo27Zto5/HN9HpdNi1axcz8kpEIBXV8+dt8NbAucar5qF9QnvY2dl9cKObAQMGDLwvBsHaAEuXLkWfPn2atO6MGTPq+BVu374dLBYLAwcOxKpVq5ha1louX74MIyMj9OjRAwcOHIBcLoelpSV69+6N+Ph4qFQqWFtbIzc3F9bW1mCxWJBKpejVqxdsbW3B5XIxZ84c+Pn54eDBg6iqqkJeXh4mTJgAV1dXyGSyOlZYLVu2ZKb5a4MJxo4dC1tbWygUCrDZbCgUCggEAnh4eKB///7IysrCiRMn0L17d2zcuJHZ9y+//BJdu3ZFTk4OunTpgj///BMymQxyuRzFxcXQ6XSIj4+HUqmEUChEQkICrKysEBISwlyo8/LyYGZmhu+//x4ODg7o2bMnU9fq6OgIGkc109Rv3x4RiEegq28t/51AbALd+fcybgUX08umo6qqCps3b2bSs9RqNRITE7F69WocOnQIe/fuZWJxc3NzkZ2djcWLFyMzMxPp6elwd3dHQEAABg8ejMTERMTGxiI6OhoRERGM9ZNAIIBMJoO1tTUTIVo7umVkZMT43QoEAtjY2MDOzg5CoRAWFhawt7eHnZ0dbGxsoFarmZFpPp/P+MJKpVIoFApwuVy0aNECvr6+8Pb2hqenJ0x+MGma+ACBWhNohf77EpCg97MdExODVatW1Vu+Y8cOqFQq/PDDD42eG48ePYJKpWrSefQmeXl5sLGxwfz585GSkoLg4GCoVCrmHxs3Nzd89NFHsLW1RatWreDl5QWJRIKEhAT8/PPPfyuO9J+ivLwcmZmZUCgUSEtLe2d5SUP89ttvcHd3x9ChQz9YfOfn58PHxweCzwRNE6wNnWuoOTfb/9L+vepuDRgwYODv0phgZdXcrx9fX1+cOnWqwfv/r1NcXEz29vZ04cIF0mg0ja774sULcnR0pCNHjpDMWUYLChfQvD3ziC1nk6XAkvx4fvRlqy9JRSp6+vQp2dvbk4uLC6WkpNC4ceOIzWZTTEwMbdu2jXr27El5eXlUUFBAzs7OFBMTQ59//jm9evWKRCIRKRQKUqvV1KtXL9qyZQs9e/aM7ty5QwKBgIRCIRUUFJCbmxu1a9eOAgMDKTY2lp4+fUq///47nTx5kk6dOkXZ2dlUWVlJTk5OZGdnR7t37yY2m01OTk4UFxdHffr0IQcHByIi6ty5M6WkpFCXLl0IAHl6etLixYtpwoQJNGPGDNq/fz9t2rSJRo0aRWPGjKFFixbR9OnTKSgoiB48eED3798nIqIzZ86QtbU1FRQUkJ+fH4WGhtKBAwdo7ty5NHfuXIqKiqJz585RYWEhqXapaL96f92DXElEHxORAxFlv/UGzCCifUR08K3luUSUWPMjm80mnU5HPB6PBAIBcTgc5ovL5er9ncvlUmVlJV29epXc3d2Jw+FQVVUVPXv2jJ48eUKmpqYkk8motLSUnj9/TjKZjCorK6miooLKysqoqqqKiIh4PB7x+Xx69eoVmZiYkIWFBd2+fZvc3NxILBYTn88nPp/PrMfj8Uin09GrV6+oqKiICgsL6dKlS8xrkMvlpFQqSalUUmFyIV2JvUI6nq7+B/MCETUjIh0RLSeiLCK6RkS8uqtxK7kUeTCSgo8HE4vFIiJivp87d46uXr1KcXFxdZazWCz6448/KDc3l8LDwyk4OJjZ3pvrVFRU0Oeff04ZGRn1tv32dwB09epVOn36NF26dIkqKiooMDCQNBoNWVhY0NWrV6mogWBnyAAAIABJREFUqIiioqLo0qVLdP78ebp//z7J5XJisVhUWVlJvr6+FBAQQHZ2dg0+T2P78E98B0BnzpyhNWvWkJWVFSUlJZFarX6v7dT+fO3aNcrIyKAePXpQdHQ0sVisD943IqKJVhPplMs7/m43dq79RQIlUC7lNr4dAwYMGPgHYbFYpwH46ruP+9/emf9NSKVS6tevH82cOZNWrFjR6LpisZh6Zvakj158RM/oGZGMqLpPNVVTNd2je/RE94RsyIbaVrelY4nHSCqVUkBAAI0fP56kUilpNBo6ceIEDRs2jFavXk1//PEHDR48mPLy8mjx4sUUGBhIYrGYZsyYQenp6bRu3To6cOAAmZqaUllZGalUKurQoQO1bduWIiIi6Pnz53Tq1Cn65ptviMPhkJmZGSmVSvLz8yNfX1/Kzs6m/v3708SJE8nb25t+//13Ki4upqlTp9KxY8coMDCQtFot9enTh4qLi0kgEBAR0cGDBwkAicViKiwspGbNmlFsbCyJxWIaNmwY7du3j6ZMmUL29vZkb29PFy9epLKyMlq5ciVZW1tTSUkJtW3bll6/fk03b96kMWPG0NSpU6mkpIS+/PJL0mq1xOFw6MiFI0TqNw6wjogSiMiYiJbpeQNyiWhS/cWqZioaPGUwqVQqKi0tpfv371Nubi55eXmRtbU1lZaW6v168eIFlZaWUllZGVVXVxOHw6GbN2+SUCik58+fk4mJCTVv3pyUSiXx+Xxis9m0Y8cO+vjjj+nGjRt09uxZcnJyonbt2pGtrS0R1YixkpISysrKIrVaTXfv3qWEhAQqKiqix48f0+PHj+n+/fvMz2w2m8zMzMjMzIzKy8vJzMyM4uLiSCqVElHNP0nnzp2jR5mPSBejR6wSEa0loq+oRoCEENFeqidWiYh0Oh05H3emiooKZl9rv6vVatq2bRsVFRURm82uc79AIKDY2FjavHkzFRQUUFhYGLPN2nV0Oh29fv2a7t27x4i5t5+DiKigoIAOHz5MREQuLi7Uu3dv2rVrF8nlcpLL5VRSUkJ3796lBw8e0Pnz50mtVpNcLqeKigqKjo4mAFRcXEy3b9+mxYsXk1KpJH9/fxKJRHWeR99z/5PfX7x4QefPn6fS0lLy9PQkCwsL2r9//3ttp/bngoICunz5MjVv3pz++OMPysrK+tv7eGfmHWqUd51rf1FERY1vx4ABAwb+mzQ09Ir/B0oCgBr/VK1W22C6Ti3LsRwCnaB+I8Pbt6qa6TSHuQ4wMTGBh4cHZDIZ+vbtC1dXV3h4eMDExAQmJibo2rUrdu3ahaNHj2Lo0KFgs9ng8/lQKBTg8XgwMjICj8eDqakp1q5di08//RRt2rSBTCaDRqNB165d8fHHHyM6Olpvxnut4f/KlSsRGxuLcePGYeLEiQBqahd3796NpKQkcDgceHl5ITs7Gx07dsTy5csxYMAAzJ49G3FxcdBoNPjiiy+wefNmCAQCmJqawt/fHwqFggkDaNasGSQSCVNHZ21tjXbt2jHlCCEhIcjJycEvv/yCixcvokdZj38fMx2BkqimRlXfVGYe1fiLvtBzXy4xRvsajQZeXl4ICAgAn89HeHg4xo4dixkzZmDZsmX49ttvsX37duTl5eHSpUt48OABXr16herqaqxatQpsNhv+/v566zaLioogl8shlUoRGxuLCxcu1Lm/oqICly9fxr/+9S9MmDABQqEQxsbGEAqF0Gg0aNOmDUaNGoUVK1bg0KFDjNE7ADx48AAKhQLXr19HeXk5Nm3ahA4dOkAqlSI5ORmHDx9GV13Xd3ppNvaZZP/Ihlwux8qVK/VOOfv7+zNxt/p49uwZgoKCEBsbq9cKTiAQNBjXWVFRgZSUFFhZWWHt2rV1mtBGjhyJPn36oEePHhCLxWjRogWCgoKYqfWUlBTMmzev3jZLS0sxc+ZMKBQKva4C/wmKioqQmpoKpVKJhQsXNuij2xR0Oh0+//xz2NjYMKEUH0JFRQUuXLiADRs2YMqUKejatStMt5g2/Fl417nWhBISAwYMGPhPQYYa1sY5evQozMzM9Hb5AzViVQjh+4mElwRxWs0FuFmzZnB0dIS7uzt4PB48PT2Rk5ODbt26QSQSQSQSwdjYGHw+H126dEFGRgYmT54MIyMjyGQyEBGMjY3RpUsX/PTTT3ViMzMzM/UmBgE19joKhQK2traYNWsWLl68CCsrK1RVVaGkpAS3bt3C0aNHodVq0b1795raUiIolUqw2WxYWFiAqEYQajQa8Hg8cDgcNGvWDGZmZkhMTGRqL7dt2wZ7e3sYGxsjJycHU6dOhYmJCfh8PgYNGoTU1FQkJiYiOjoaISEhEKYL/13DOphArQhU0sCxTCFQQv3lvGoeEs4nYNasWejVqxccHBwgFArRqlUrxMbGQiwWY86cOY167R47dgwhISFwc3NDp06dMGrUqDr3P3v2DFOmTIFCoYC9vT2mTJmC06dPY+3atZg0aRK6desGFxcX8Pl8ODk5oUuXLpgwYQI6deoEU1NTpgmtMfr27Yv+/ftj+PDhUCgUiIiIwJo1a/Dy5UtmnXzkv/9n8K+bEEKsOLUCnp6ejIB+28A+PT39nbGnpaWl6NGjB8LCwvDnn3/WuU+pVNYR4bW8ePECbdq0QZcuXVBSUgKgpu7zp59+YkIMlEolvvrqKxQWFuK7775Dz541bgbV1dWwsLBgLOX08dtvv8HNzQ2jR4/+j7kDVFVVITs7G+bm5hg0aJDe1/k+lJWVIS4uDv7+/vUicBuisrISV69exaZNm5Ceno5evXrB1dUVfD4fLi4u6NGjBz777DN8//33GPNoDPg6vv5Pw7vOtb9uAgjwBb74W6/TgAEDBt4Xg2BtAps2bYJKpcK+ffvqLG9QKIRRTcNCrUdoMz1/9l8RFO0UkMlk8PHxgbm5OSQSCROHKRaLERoait69e6NDhw6QSCQwNjZGmzZt8Omnn6JVq1ZYsmQJ/P39sWLFCrRu3RpeXl44dOgQs3+ZmZkYN24cdDodiouLcf36dRw+fBibNm3CsmXLkJiYCCKCi4sLAgICYGxszHjA1jaz1AYQ+Pj4wNvbGy1atKjjNhAcHAytVgsOhwONRgMTExO4urqCw+FAKBQyTTIsFgtarRYymYwxPB84cCCmTp2KBQsWYObMmWjRogXYbDYEWgHYFeyaZip661iaUI0HK6jGMF9CoF/qH18++HiCuuKhuLgYBw8exMKFC9GpUydwuVwYGxujefPmSEpKwuLFi3HkyBGcOnUK3bp1g0ajwerVq1FVVYWHDx8yrghXr15lBJWHhwdCQ0Mhk8kYIa/VapkGqtrX7+DggODgYKSkpKBly5YYOnQo7O3tsWDBAr2fuUePHmHo0KEwMjKCVqtFenp6o3n2k+5N0t+o9g6xWmsEr9PpsH37dtjZ2UEgEMDNzQ2HDx8GUOM16+rq+s7zpLq6Gp988gnc3Nxw9+5dZrlWq623769fv0ZQUBBSUlJQWlqKnTt3IikpCTKZDKGhoVi+fDlOnToFc3NzRjxv3boVnTrVuBn8+uuvcHNze+c+/fnnnwgODsbIkSPfue77cujQIbRs2RIhISFMxPHf4cmTJwgMDESvXr1QWlpa7/6qqircvHkTW7ZswcyZM9GnTx80b94cfD4fDg4OiI6OxqRJk7B+/XqcP39e7z9jj/EYPJ2eMI53nWvvOLcMGDBg4D+NQbA2kQMHDkClUmHChAl48eIFAKAbuumfig0j0Mp3yIUqgskuE5iamjKCTq1Wo3nz5tBoNDA1NUV4eDjGjRuHjRs34siRI1AoFMxU44IFC5CYmIg+ffpg2LBh2LhxIxITEyEWi2Fra4vIyEjY29tDIBCAz+fDxMQE1tbW8PT0RGBgIKKiotChQwew2WxwOBx06tQJrq6uMDc3h4+PDxwdHaFU/tt8n81mw8HBAQKBAM7Ozoyort1/qVQKlUqFDRs2IDw8HPHx8QgODgaPx4OZmRk6d+4MpVKJ/v37w8LCArdu3YJOp8Pq1athb2/P2HitX78eANClqkuNH+SH3KoI7tfc39kpfvr0aahUKsyfPx8rVqxgHBpqR5I7duyIAQMGYOjQoUhMTISlpSU4HA6ICObm5ujRowcSEhIQGBgIoVAIExMTjB8/HqtWrcKxY8dw+/Zt3LlzB3fv3sXNmzexf/9+ZGVlQSqVwtPTk0kDq43HraiowObNm9G5c2eIxWJIpVKkp6e/c3Tw5MmTMDMzw8BTA8Gr5L27NKW64dSi6upqrF+/Hubm5uDz+QgKCsL58+dhbm7eoPfn2yxYsABWVlbMrISbmxsuXbpUZ520tDT4+/sjJSUFSqUSAQEBWLhwYR0jfZ1OB3Nzc/z+++8AaiJbo6KiAAATJkxgbNTeRXFxMRN9+k/w+++/IyYmBjY2Nvjuu+/+kQCCy5cvw87ODpMnT0ZlZSV+++03bN++HZmZmUhISIC3tzeEQiG0Wi06dOiAtLQ0rFmzBqdPn26w3EIfR48ehele0w8+t970YTVgwICB/yYGwfoePHjwAP369YNarcbsr2brH6loqmAFgUoJLLOaHPDWrVtj5MiRWL58ObZu3Yq9e/di/fr1WLhwIcaPH4/evXvD1NQUGo0GSqWSCQIQiUQQCASwt7eHo6MjHBwcIJVKGUuk2pFQMzMzODs7o1WrVmjXrh1iY2ORkJAAPp8PLpeL1q1bIycnB0KhEPv27cP169fx+PFjKBQKLFq0CB9//DGOHDkCJycnJjEqIyODGaE0NzdHy5YtIRaLIRaLoVQqmaQuoVCIlJQU7Ny5kxmpTklJgYmJCbhcLjp27IibN2+isrISe/bswYABA8AJ4NSYmX/IRfUVC4p2Cjg4OGDnzp2NvqfHjh2DVCpFcHAwM2Lq5eUFExMT5nibmZkxx5vNZiM5ORkzZsyAq6src9weP34MU1NTFBYWNvp8L168gFAoZERJWloaOBwO5HI5ZDIZwsLC8PXXXyMzMxNRUVHvFENHjx6FSqXCTz/9hOXLl0PRToHwZ+Hggw8BBHWOiwAC8HQ8GG01ws6njR+XyspKfPnll5BKpTA2Noa1tXWTShhq+f7776FSqbBnzx74+fnhxIkTqK6uxpEjR9CzZ0+w2Wx4enoiMzOz0ZHj6OhoJkXpyJEjCAoKAlCThHXixIkm78+vv/4KlUr1zvenMV69eoVp06ZBLpdj2rRp7yUU9aHT6fDHH39g9uzZEIlECAkJgb+/P0QiEaysrNC2bVuMGTMGq1atwvHjx5l/lD+EwsJCpKSkQK1WY9aeWRDqPryE5M2kKwMGDBj4b2EQrB/A8ePH0Xxt84YbE8IIpCSQgkCBBDrQsGCVzJTAzMwMQqGQSXsSCAQQCoWMmKxtHDI1NYVEIkFQUBC6du0KY2NjxMTEQKlUIisrC+vWrcPPP/+Mo0eP4uDBg4wwmDJlit6M+t27dyM8PBwbN24Ei8XChQsX0KVLF6xevZpZRyAQoEWLFtixYwfi4uLQu3dvZgSXx+PB1tYWLi4uePToES5fvgwejwehUMhMh3O5XKxbtw43btyATCaDi4sLWCwWFAoFpk6dipcvX2Lnzp0YMGAAlEolvL29oVQqYWxsjMjvI997mptXycPiisUYM2YMZDIZLC0t0aVLF1y5cgUXLlzAd999h/T0dMTExMDd3Z05viwWCx06dMDKlSuRl5eHwsJC3Lx5E/3794dcLseECRNw4sQJdOvWjfFJFYvFkMvl+Oijj5CWlgY3Nzfk5OQ0OiJ65MgR+Pn54fHjx1i4cCFatGgBS0tL5rjNmTMHBQUFUCqVuHLlSqOfw4MHD0KpVOLnn3/GuHHj0KxZMyYF6wme4At8gQQkoBM6IQEJ+AJf4AmeYPTo0U2eIi8rK0NGRgZ4PB5YLBaSk5P1NvHp4/Dhw1CpVNBqtejZsyc0Gg08PDzg6upaLwCgIWbPno0xY8YAqBlJ9vb2xvXr12FpafnedakJCQn1krKagk6nw8aNG2FjY4PY2Ng65Q5NffyjR4+wb98+LF68GIMGDUJgYCAkEglMTU1hbGyM7t27Izs7G3l5eSgqKnrvfWzsudeuXQsLCwsMGzaM2faH1N43NCpvwIABA/8NDIL1A4lDXMN/2o9TTdd6OYG+oZqIw1v617XaZ4VBgwbh888/x+rVq7F161YcPnwYFy9exP3791FSUsKMst29e7dOWUDHjh2xYcMGCIXCBkdfRo4cCZVKhZYtW+LAgQN17ps7dy7TSNSqVSvY2Nhg8+bNaB3dGpnIRJwuDrSVIPpRhKkvpkJkJ4KFhQWkUikkEglkMhmaNWuGhw8f4vjx4xAIBHB1dUV0dDTEYjHYbDaGDRsGiUTC1HK6uLhg69at+Pnnn5GUlAS5XI7WrVtj/vz5OHToEBQKBSQSCeLj4+Hk5IRpD6dBCOE7u+BZYIFXyYNisgLh4eGYP38++vTpA4FAwIw0KxQKdO7cGZ9++ikGDx4MCwsLdOzYEZcuXcKhQ4egUqlw8OBBXLlyBXFxcVAqlZg2bRozKldUVISgoCDweDycOnUKOp0ODx48wPbt2zF9+nQ4OztDIpFAJBIhKCgII0eOxOrVq3Hu3Dm8fv0ar1+/xsCBA6HVahmT+3379qG6uho3btyApaUlrK2t4ezs/M4mpz179kCpVGLHjh3o1asXgoODmywkHz9+DLlc/l7C648//mCcKXg8HsaPH1+n8etNdDodzp07h08//ZQJvvDw8MDFixdx+/ZtKJXKBh/7Nvv27WNGVS9dugQ3Nzd88cUXTOzx+3Du3Dmo1er36uA/c+YMQkJC0LJlyzr14Q3x7NkzHDp0CFlZWRg2bBjCwsKgUCggl8sRGhqKoUOHYtmyZdi3bx8GDx4MFxeXOlG7/yTXr19HZGQkWrZsqXc0ula0NuXcMohVAwYM/E9jEKwfSCd0avrYRDsCLdF/H3t7TQ2pmZkZ3NzcEBoaiu7du2PQoEGYNGkSFixYgNzcXOzYsQP5+fnw8vJiurgzMzMxYsQI+Pv7Mw0yb1NUVASFQoHMzEym4//27dsAgPj4eCbF6NmzZ+AEcOB82RlUVtNl/+Z+cl9zwSpnQbRbBHGUGDweDw4ODrh37x5mzpwJoVAIlUoFuVyOYcOGgcvlwsXFhRGLNjY2iIqKAp/PB4fDga2tLdLT03Hv3j0ANclGtUlQ4eHhaNOmDdNtfhIn0R3d9U5zG702Auc1B2aHzaD6WAWBQAALCwvweDy0adMGX3/9NXr16gW1Wo2IiAgmRax169b1YiVzcnJgbGwMmUyG2bNn10kmqqiogL+/P0aOHInZs2ejV69e9Y719u3bERUVhcLCQuzbtw9z585F3759YWdnBy6XCy6XC4FAAH9/f+zdu7eeaLt27RpkMhm4XC769+/f4GevNsN927ZtCAwMRO/evd87dWjSpElISUl5r8eEhYVhw4YNSElJgbGxMUxMTDB37lxGAF65cgXTpk2Ds7MztFot0tLScObMGXTq1Am2trZISUnBsmXLkJSU1OTnfP78OUxMTPD69WvcunULdnZ2CAwMfGepR0O4ubk1qUHqyZMnSElJgbm5ObKzs+vNUBQXF+Po0aPIycnBqFGjEBUVBQsLC4jFYrRu3RopKSlYtGgR9u7di4cPH9Yp7Xjx4gU6duyIqKioeo4K/wRlZWWYNm0aFAoFFixY0Gg6VmPnlgAC8MFHd3Q3lAEYMGDgf5zGBOv/08EB70JCkqavzKKa8UU9sJ+zqaq6ip4+fcoY19cauHM4HCopKaHTp0/Ts2fP6NmzZ/T7779TTEwMsdlsEovF9PLlS1IoFDRq1CgKDAxkEpDe/JoyZQqtWLGCTp06RTk5OeTn50cDBw6kc+fOUWpqKhERfa/4ntiH2XSdfZ2IQ1RBFXX2s8qoJrXpZdRLoiAi2SwZrem4hmJiYujx48dUXl5Obdu2pXbt2tGIESOourqaiouLycLCgthsNhUXF1NlZSXNmTOHzM3Nac+ePbR48WLav38/2dnZ0bp168jT05NevXpFzZs3p/nz5xObzaa7d+/Ss6vPKPhqMAnvCynPIY8KlAWkk+hICinZldhRx2cdyd/On1yXu5KNjQ2x2Wy6ffs2jRo1ijIzM2n58uUUGBhIEyZMIGNjYxIIBMTn8xkT/pMnT9LMmTPp5MmTlJiYSD/++CNFRkaSWCxmXv+kSZPI0tKSFi9eTKWlpeTo6Ehnz54lLy8vZp2AgADKz88niURCzZs3p0uXLtHVq1epqqqKxowZQ76+vjR69GgyNzenCRMm0NWrV8nW1pa8vb3Jy8uLmjdvTgqFgnQ6He3YsYO+++47io2NrfM+/PDDDzR06FBavnw5jR49mnr16kUzZ85kTP2byrhx48jJyYnS0tLI0dGxSY/p2LEjHTlyhHJycujzzz+n1NRUmjhxIn322WekVCqpurqaYmJiaM2aNeTv788kLCmVSkpLS6OffvqJduzYQePHj2/yforFYrK1taWLFy+ShYUFvXr1ii5fvkwRERHv9XpradWqFZ04caLO+/YmlZWVtGzZMpo9ezbFx8fTqVOnqKCggHJzc+ny5ct06dIlunz5MhUVFZGbmxu5u7uTh4cHffzxx+Th4UFWVlZ1kqXe5t69e9S5c2cKCAigZcuWkZGR0Qe9jobYv38/DRkyhNzd3ens2bNkbW3d6Pq+5EubaTM9paf0DX1DF+kiFVERyUhGnuRJSZREKlL9o/towIABA/84DSlZGEZYkYlM8KHHz7CIQLuoxnKpkmpsYYQEuq5nfLWUkP4yHVVVVTh58iQyMjLQvXt3uLq6QiwWM0b7IpEIzZo1Q+fOnTFmzBhIJBIUFBTgzp07MDExwdChQxEcHIylS5di2rRpGD58OGJjYxEVFYUWLVowBv4cDodxCrC0tAQRISwsDB23dYTRa6Omjxj/NbLJ/4QPGxsb8Hg8hIWFQS6XM44HwcHBzGjlkiVL9HpKlpeXIyYmhmlm4nK58Pb2RmxsLHx8fGBiYgK1Wo2oqCiMGDECWVlZ2L9/Px49etSkzmydTodly5ZBIBBAIBBg+vTp6NOnDxwdHTF27FhIpVLY2NhArVZj6dKljJXQ9u3bYWZmhlOnTgGoGf3VaDR1ptwXL17MWCzV8vr1a1hZWSEyMhISiQRxcXHYu3cvMzpXUVFRx0S/oqICZ8+exapVqzBixAjY29uDw+FArVYz0+/r1q1DQUEBAGDDhg0wNzfH6tWrmZG/v8P06dMRHx/f5PUvXboErVaL33//HXPnzoWvry8UCgU0Gg1ja7Zjx456jxs+fDiWLl2K169fQ6VSwdnZGY8ePWry8yYnJ2P58uUoLCyEQCDQO7rdVObPn8+4MrxJaWkpli5dCktLS9jZ2SEsLAy2trYQCATw8vJCfHw8MjIysG3bNty5c+eDfF1PnDgBtVqN+fPn/yPOAm/y+PFjxMfHw8bG5p1BJwYMGDDwfxEylAR8GI/xWL9gfUIgX6qpW5VQjRH3Hv2ij/Oag3bx7Rq8eFVVVeHChQtYuHAhYmNj4enpCalUytSD1lpWNWvWDAqFArt378bTp0/1bqu0tBTdunVDq1atGHsllUoFdbRaf2PTUgL5EMiYQIkNyNaXBEGIAETENIsREXx9fRnhXVxczOzDq1evcObMGaxbtw6TJ0+GRqNhBC6bzYajoyNjxRUREYHc3Nz3nuqupaCgAIMHD4ZSqcT06dMxduxYJoVoypQpMDIygqmpKYKCgmBubo6VK1fWESFbtmyBubk5zp49i86dO2PFihV1tl9WVgaNRoNff/0VFy9exNixY2Fubg6VSoW4uLg6r7uWM2fONOgd+uTJE6hUKpw7dw7Xrl1DRkYGOBwOzM3NIZfLIRbXlGFERUVBLBZj9erVf1v0PH/+HCqVCpcvX37nugUFBVi8eDF4PB4kEgkGDhyIvXv3MtPNFy9ehL+/PzgcDtzc3OrUTKalpWHOnDkAamqlBwwYAHt7e1y/fr1J+5mdnY1+/fqhtLQUbDYb69at+4BXW8P8+fPRt29fbNiwAZMnT0bXrl1hY2MDNpsNY2NjhISEYPr06fjxxx9x48YNvc2KH8KmTZugVCr/cTFZXV2N7OxsqFQqjBs3jglgMGDAgIH/v2EQrH+DBn1Ym3BjgYWuVV3h6+uLJUuWvNfzfvHFF+jevTuWL1+OFi1aMN6hbDYbLFaNTZZGo0F4eDjGjBmDLVu24O7du6iqqsInn3wCGxsbDB8+HDExMTWxntV6XsNmAv1IoCGNCNYqAv2LmOeuFZ8mJibgcDjQarVwcHCARqNhQhHUajUCAgKYxCwfHx+4ubnV8fh89uwZsrOzER4eDqlUisTEROzatavRWrxaiouLMXnyZMjlcowbN45pmNLpdMjOzmaE3yeffILOnTvDw8MD69atQ2BgIPz8/HD8+HFmW//617+gVCohl8vrWRgVFhYyVmNWVlb49NNPce3aNUZc6WPVqlWIi4vTe19ycjJGjx5dZ9l3330HNpuNQYMGwdLSEl27dmUCJWqPaVhYGEaPHo3c3FxcvHixScfoTb744gsmPeptnj59ihUrVjDvQ0JCAjp06IDZs2c3uL1jx44xwRGtW7fGjRs38Pnnn2Pq1KkAgICAAOTl5eGrr76Cubm53qjbtzl37hycnZ3x4sULEFGT6j4rKytx5coVJv2pZ8+ecHV1BZfLhUwmQ48ePTBx4kR06dIFUqkUGRkZjaaefSg6nQ6zZs2CtbX1PxIu8CYXLlxAYGAgAgICcO7cuX902wYMGDDwvw2DYP0b/N1IzJM4iVu3bkGlUr3XxexNt4DDhw/D19cXfn5+OHDgAK5du4ZVq1YhOTkZPj4+UCgUTDlA7Yidu7s7+Hw+FK4KcCu5je/p5EYEKwischZ4Gh64XC5atmzJxMU6OjqiXbt2iI8uWU1nAAAgAElEQVSPx/DhwzF27FiMHj0aMTEx4PP5jLDmcrn1ms569OiBwYMHY/LkyUhPT0ffvn3RrFkzyGQyxMfHY/fu3fVGvsrLy7Fw4UKYmZkhKSmJ6YDX6XTYsmULfH194e7ujvXr1+Pbb7+FWq1G//79sWzZMmYU9uuvv4ZarUZSUhIzZd2/f38IBAJcuXIFlZWV+Pnnn9GzZ0+IxWLExMTA0tISv/zyC7MfFy5cgKOjo973bcSIEZg3b1695b/++issLS3rNHnVolQqwWKxEBYWhubNm+P+/fvMfU+ePMHu3buRkZGBmJgYODk5QSgUwt/fH0OGDEF2djby8/MbHaV+9eoVLC0tmc9fUVERVq9ejXbt2kEikSA2NhY//vgjs43t27cjPDy8we3VsmPHDlhbW4PD4cDFxQWDBw8GUGMtlZOTw6yjUqneaehfWVkJkUiE3NxcsFisOq+nNv3pxx9/xMyZM9G7d294enqCz+fD0dER0dHRmDx5MpP+lJSUhKVLl2L16tWwtLRE//79mxyB+r6Ul5cjMTERPj4+ePDgwT+23ZcvXyItLQ0qlQorVqz4j8XOGjBgwMD/JgyC9W/yIX6G3HIuluv+bRGzYcMGODk5vZcxeEBAAHbt2oWysjKYmJggOTkZixYt0rtuZWUlrl+/jm+//RaDBw9Gq1atwOVyQePo3T6n7xCsVErgTeEhJiYG2dnZUCgU9SJsa7ly5QrjXWptbY2JEyeiuroaFRUVePjwIS5evIgDBw5g06ZN+PLLLzFjxgykpqYiLi4Obdu2hbu7OyQSSZ3aXicnJ7i5ucHExARarRZDhgzB0qVLsW7dOkyZMgVOTk7w8PDA+vXr61zYi4uLkZqaCpVKhYyMDHz00Ufw8/PDyZMnMW7cOCiVSixYsADx8fHo2rUrRCIRVCoVWrVqhRUrVjB+lrm5uQgKCmKm56uqqiAWi/VmygcFBdU7NlVVVfDx8UFubm699TMzMyESiWBtbQ0jIyNs3rz5nZ+L58+f4/Dhw1i0aBESExOZ6E5PT0/069cPixYtwqFDh+qI4y+++AItW7ZkEra6deuGjRs36rWeevXqFUxNTfWWPLyNTqfDt99+CxMTE7BYLPTr1w/z5s3DgAEDmHVOnToFtVr9zlmG0NBQpi76s88+Q0JCAry8vJj0p44dOyItLQ25ubmNpj85ODjA1dUVAQEByM/Pf+dr+FCePn2KkJAQdO/evckWXk1h27Zt0Gq16Nu373vVARswYMDA/3UMgvUf4H38DAU6AaxmWGHmzJl1tjFgwIAGp5L1MX/+fCQnJwOosRtKTU1FQkJCkx9vZmaGriVd3y2v3yVYQVDuUGLRokVQKpWYNGkS8vPzce3aNRQUFDA+snv27IGxsTFsbGygUCiwdu3aJu/r2+h0Ohw/fhydOnUCl8uFsbExQkNDMWXKFEyZMgVRUVEwNTVlYmr/v/buOq7K6wED+HNpLiGNtAIKEjpKxEJFxRYTu5252bO7FVTEYLpws8GcjTV7dqDibMSJHYDU5d7n9wd69Uqr23C/8+VzP3LfOO+5L849nPeElZWVchomBwcH+vj4MDg4mB07dmSHDh1ob29PBwcH5SP3ESNGcNu2bcpFDgwMDNiwYUPlkrLvy87Opqurq8o0S/Xq1eNvv/2mcpxcLqe+vn6ulZaioqJYvXp1lf6oCoWCkydPppOTEy0tLenm5qacJ3bv3r3Fvl/p6ek8ffo0ly1bxr59+9Lf3185/ZeNjQ21tbWpqanJYcOG5dnK+6GGDRsyJiamyNf/+eef6evrSwMDA2pqalIqlaoE3tu3b9PFxYXDhw9ndnY2ExMTuXv3boaFhbF79+45fWOt1IkRoGSVhPYX7VnlRhUOTBjI2ylFWy72/v37bNy4MdXU1Pjzzz//ra2S8fHxdHJy4siRIz/bdRITE9myZUs6OzszNjb2s5QpCILwJSkosEpy9ufN19eXZ86c+ZvmJ/jynMEZzMRM7MROSCBBOtKV+3ShC4JohEYYjdGwSbJBtWrVMGbMGPTq1QsA8Pr1a/j5+WHUqFHo0qVLode7d+8evL29kZSUhGnTpiExMREnT57ElStXCj334cOHcHd3R9WnVbFdsr3gg8cBuA9gRf6H2Jyzweug17C2toalpSVSUlKQnJysfKWlpQEANDRyZkpzcXGBjY0NDA0NYWBgAENDw1zf5/deW1sbp0+fxsiRI/HgwQPMmDEDVlZWWL16NVauXInMzExYW1tj6tSp6NChg3KKIZJITU1VTg/2/uvJkyc4duwY/vjjD+jq6iI1NRVyuRwAIJFIlFOImZub46+//kL79u3h5OSknDYsLi4O69evx8GDB2FiYoIpU6ZAJpNhxowZynt0/fp11K9fH3fv3lVue/bsGdzc3BAbG4tKlSop6zl27FjExMQgPT0dX331FcqWLYvIyEgcOXIELVu2RHR09EdN65SZmYk9e/Zg3bp12LlzJypUqAB3d3doaWlh//79uH37NiwtLeHl5aWcasvLywv29vYqUzUtWrQIZ8+exc8//1yk627cuBFr1qzB+vXrMXXqVEybNg0aGhoYMWIEAgMDER8fjzNnzmDLli3IyMiAsbExPDw84OHhAb1aejhW8xiOGR6DPFsO6L4r9+1/Vw3REKMxGn7wy3XtjIwMhIeHY968ebCzs0PTpk0xderUYt+7otq/fz86dOiAWbNmoXv37p9cXnZ2NhYtWoRp06ZhwIABGD16NHR0dD5DTQVBEL4sEonkLEnfPPeJwFp8RZ3P8MaNG6hZsyaioqLQvHlzAEBcXBzq1KmDY8eOoXz58oVeKyAgAJMmTYKGhgbGjx+PCxcu4MmTJ9DT0yvwvNjYWMycORM2B22wGqsLvkgRAitWAoYDDVGzZk24u7vD3d0dbm5uqFChAsaPH48FCxbAyckJOjo6mDdvHvT09JRh9sNwW9D7V69eQaFQAACMjY1hY2MDAwMDvHr1Crdv34aRkRHKly+P5ORkXLt2DTY2NggMDETdunVhb2+vEnwNDAygrq4OALh27Rp++eUXrFixAnK5HBkZGQgKCsLRo0ehp6eHSZMm4fjx41i3bh3KlCmD+/fvIzQ0FFlZWcrAe+7cOWhqaiI9PR1SqRTZ2dnw9vZWhtonT57g1q1bGD58uHLbggULUKpUKURFRUEikYAkhg0bht9++w0vX75EREQEHj16hISEBERERAAAfv/9d7Rp0wYbN25EzZo1C/07IpPJsH//fqxbtw6//fYbPD090a5dO7Rq1QoWFhYqx7m6umLq1KnQ1tbGuXPncP78eZw7dw4ymUwlxJqZmaFTp05ISkoq0vyv0dHRCAsLQ7du3XDlyhUcOXIEcXFxAHJ+ialevTpat26NcuXKITIyEikpKdi8eTPWGa/DcAxHOtJB5P9vkQQS6EIXYQhDP/QDkBP8N2/ejGHDhsHLywvdu3dHjx49cPXqVZib/z3zii5fvhzjxo3D+vXrUatWrU8u7/Tp0+jbty8MDQ2xdOlSuLq6fnolBUEQvlAisP6Lzpw5g4YNG2LTpk2oUaMGACAqKgrff/89/vjjD2hraxd4/rx583DlyhVERkbCwsIC5cuXR2RkJKpVq1bgeXPnzsVff/0F6wXWmIiJyEBG7oOy37wmIyewLgeg8eb1vjRAc5omer3sBRcXFyQnJyM+Pl45wbpCoYCuri4cHByU4aFChQqQSqVFu0nIaRGeOnUq1q9fj2+//RZdu3ZFSkqKMmSWKVMGTZo0QenSpZXh9vnz5/jzzz/x559/4uHDh9DT01MG+dTUVKSkpEBTUxMkoVAoYGZmBgcHB1hZWSErKwtnzpyBuro6Xrx4ARMTE4wcORK6urpYvXo1Lly4AG1tbezevRsuLi6QSqXYsWMHRo0ahbNnzyIxMRGVKlXC9u3b8fLlSzx9+hSrVq2CTCZDuXLllAtA/Pnnn5BKpUhPT4eJiQlkMhnS09OhUCgQHByMihUrYt++fXB1dUW7du1UWnR79uyJLVu2oGrVqrnul1wux6FDh7B+/Xps2rQJzs7OaNeuHVq3bg0bG5t87/OqVavw/fff4/DhwyotqklJSTh//rwywJ4/fx4JCQnw8PBAzZo1lS2xtra2uHHjhvJn/3ai/ZSUFKirq6Ndu3bKifbXr1+PpKQkSKVSbNiwAaVLl8ayZcvQsGFDDBs2DOuM1uHV+FdIV0vPt74fkkKKMIShelx1DB48GI8fP8aCBQtQuXJl+Pj4YPLkyWjfvn2RyysquVyOkSNHYtu2bdi+fTvKlSv3SeW9evUK48aNQ0xMDObMmYPOnTsXuBiBIAjC/wMRWP9le/fuRadOnbBv3z54enqCJNq0aQMbGxtlq1p+3u8WEBgYCENDQ7i7u6NSpUqIj4/HX3/9hQsXLiA+Ph6hoaEwMzODq6srNm3ahEaNGqHD4A5wgEPegXUScsLq+ya+2f4eSaYEm85swpFNRxATEwN9fX2EhIRg48aNuHPnDvT19REcHAw3NzfEx8fjypUruHHjBqysrJQtsW9bZV1dXVWCbHJyMsLDw7Fo0SJ07doVY8aMgba2NpYuXYp58+ahatWqGDduHLy9vQu8T6mpqdi2bRvWrFmDAwcOwNTUFE+fPkVQUBDatGkDb29vpKenq7Tqvnz5Evv27cOuXbugoaEBknB3d4eBgQEePHiA27dvQy6XQ1tbG9nZ2dDT00NGRgbMzc1hb2+PS5cuoUqVKnBwcIChoSG2bt2KWrVqoUqVKtDT08OUKVPQsmVLtGvXTtlCfvz4cQDAyJEjoaOjg6dPnyoDl0KhUOnCkJ2dDblcDkdHRzg4OMDU1BQymQz379/Hn3/+CVNTUwQFBaFFixaoWLEizMzMCn2ULJfL4enpifnz5yM4OLjA+9m9e3ckJSVBQ0MD169fV9ZJKpXCxsYGHh4eqFGjBho3boyUlBT07t0b586dU5aRmZmJ6tWro3r16vjuu+/QvXt3xMbGoly5chi1cRT6uPSB7HtZTst+HID2eNfKfxdAWQDvP0gYCWA8oJGpAf0m+pgWMg19+vRBeno6WrVqBXt7e/zwww8Ffv6PkZqaio4dOyI5ORkbN26EiYnJR5dFEhs2bMDgwYPRqFEjzJo1C6ampp+xtoIgCF8uEVhLgLVr1+K7777D0aNH4eDggBcvXsDLywsLFy5Es2bNCjy3cuXKCAwMxKZNm5CQkABDQ0M0aNAAFSpUgJ2dHWQyGZ4/fw5ra2s8fvwY8fHxWLt2LdTU1ODq6oqMNRm44nwFlOT/s86XHGic3RjbtXP6wb5dUjQ0NBRpaWmQSCRo3bo1Jk2aBDc3N+Vp2dnZuHXrFq5evapsibt69aoyyFaoUAGZmZk4ffo0qlevjrlz58LKygqRkZGIjIxEUFAQxo4dCw8PjyJV8/r16/jll1/w66+/wtTUFG5ubrh//z7i4uLQrFkztG/fHkFBQXkuk3ny5EnlLwPa2trw9/fH0qVLYWRkhObNmyM2Nha9evXC6NGjcezYMYwePRorV67ExMUTkdoqFRreGkiWJOPPU3/CR8sH5Y+Vx59H/8SNGzfg7u6O5ORk3L59G2lpaSAJLS0tZdcFTU1N3LlzB0FBQTAyMlLp16ujo4OrV69i3bp1cHFxwZ07d6CtrQ1nZ2dlK+qzZ89U+upqaWnluXTv+68rV64gJiYGe/fuhZ6eHm7evKnSWnrlyhU8evQINjY2ePHiBUaMGKFsNTUzM8Ply5dVWmKvXr2K0qVL4+nTp5gwYYKyNdbExAQvXrxA8+bNYW1tjZ9//hkPHjxAp06d8MeIP4DmALYCUAOwB0A6cgdWGXK3+CuAJrIm2Ka9DUlJSWjcuDH8/PywePFiZR/qz+X+/fto2rQpvL29sXTpUmhpaX10Wbdv38bAgQNx7949REVFoXr16p+xpoIgCF8+EVhLiIULF2Lx4sU4duwYzMzMcOLECYSEhODMmTN5rgeekZGBBQsWYMqUKTAzM0O7du2wd+9eyGQyXL58Od/rZGVloVSpUnjy5AlOnDiBhScWYvvw7UDRn9Ar6Sh0cETtCHyR8/fn/PnzqF69OjQ1NaGvr4/p06fjwoUL2LBhAwwNDdG2bVu0adNGJbx+WLfIyEjMnTsXhoaGcHR0RGJiIq5fvw6FQgFra2s0bNgQNWrUyLNF9n3JycmIjo7GihUrcOPGDXTq1Aldu3ZFxYoVlcckJSUhOjoaa9euxe3bt9G6dWu0b98e1apVU+mbGRISAkdHR+zevRtpaWlIS0vD8uXL0bx5cwwdOhQrVqyAlpYWZs+ejYjjEcBoIM42DpQTcm25shxd6kIBBbATWO60HKGOoWjbti2OHDmCqlWrYt26dVBXV1e29A4dOhSWlpZo1qyZsi/vq1evcPPmTZw/fx7Xr1+HTCZDVlYWHB0doampqWwhTk1Nha6urjL86uvrQ09PD9ra2tDU1IREIoFEIoFCoUBWVhZevnypfD179gxAToufmpoapFIpjIyMYGlpCQcHB5QpUwampqaYNm0ali5dCmdnZ2XgNTIyUvYNBnL6xh44cADt27dHp06dcP78eVy8eBEmJibw8vKCp6cnDh8+jNu3b2P+/Pmo3rI67GmPLLWsdz/MD/tR30X+gRWADnUwOmo0Fo5fiCFDhmDMmDGf/ZH62bNnERISgm+++QYjRoz46PKzsrIQHh6O8PBwDB8+HEOHDv2k4CsIgvBfVVBgFdNa/cNGjx7NypUrK5dXnDlzJmvUqJFr9aKDBw/S0dGRzZs356FDh2hqasqnT59ST0+POjo6Bc77eOHCBVaoUEFlW/jrcErSirdil062Dpfw3VyyW7ZsoYaGBk1MTBgQEKAyR6RcLuexY8c4ePBg2tjY0N3dnZMnT+bVq1eVx8TGxtLLy4t+fn48cOAAHz58yBEjRtDExIQ9e/bk3r17uXHjRk6dOlVlcnhHR0c2bdqUo0aN4ooVK7h06VK2a9eOpUqVYosWLbh161ZmZWUVeu9v3brF6dOn08PDg3Z2dhw+fDjPnj1LhULB48eP08bGhg8ePGBYWBgNDQ1pZGTEjh078vnz5/zuu+9Yvnx52k23y7mP2YXcPTkoVUjpMt+F+vr67N+/f66FEE6ePElzc3M+ffqUZM4cthMnTqSLiwsdHBw4cuRInjt3jgqFghs3bqSlpaXKakdyuZzJycm8f/8+4+PjefLkSe7atYsRERHs378/GzRoQHd3d5qYmFBdXZ2Ghoa0srKig4MDLS0tqampSVNTU+ro6FBNTY36+vo0MzOjtbU17e3tWaZMGUqlUpqYmNDKyoomJibU1dWlmpoaDQwMaGtry4oVK7J27doMCQmhtrY2Z82axRUrVvC3335jdHQ0IyIiOGjQINarV4+GhoZUV1enzngdqmWoFTy12h3kLE9sDcIGRDcQT96bPi5NwvLLyqv8/fqcNm7cSDMzs0IXPCjM4cOH6ebmxoYNG6qs9CYIgiDkBjEPa8mhUCjYo0cPBgcHMysri3K5nHXr1uWECROUx6xZs4YWFhbcsWOHctvbRQR8fHxYvnz5Ape7/OWXX9iuXTuVbc+ePaPuEF1qZmoWHrayQW2ZtkpYDQsLo5qaGo2NjdmtW7cCl7iUy+U8evQoBw0aRGtrazo5OdHR0ZEODg6MiYlhYmIiv/32WxobG3PgwIHKFavyIpPJeO3aNS5evJiBgYGUSqXU0dGhhoYGHRwclEG2sMnkPxQXF8cxY8awbNmyLF++PCdOnMhevXqxSZMmVCgUTExMZIsWLWhgYEAzMzPGxsayTnQdqqWpFXzvPvxKBVvGtsx1/VevXtHR0ZGLFi3i9OnTWbFiRVpbW3Pw4MH8448/VOZsfSs6OpqlS5dmXFwcs7Ozef36dW7evDlXwHd2dmZISIhy9adLly7l+nkpFAr6+/tz7dq1JMmsrCw+e/aMd+7c4aVLl3j06FHu3LmTffv2pa+vL8PDwzlx4kQOHTqUPXr0YLNmzVi9enV6eHjQ3t5eufqZRCKhmpoatbS0qK2tTS0tLaqrq1MikVBbW5uGhobUWJvHymsfBtYUEKdByEA8BNEKRH3VczopOhXpZ10cCoWCs2bNoo2NDc+cOfPR5Tx9+pQ9evSgjY0NY2Ji8vx5CoIgCKpEYC1hZDIZmzZtyo4dO1IulzMpKYlWVlY8cOAAly9fTjs7O8bFxamc83YRgaFDh9LX17fAVYOGDRuWay34uXPnsmPHjrRrYUf1LeqUZEionqGuEgA0MjUoSZfwq1tf8TRPk8z5H3jv3r2Vk+uHhYUV+X++N2/eZGhoKE1NTVm7dm2am5srW+m6d+/OBw8eFHh+cnIyf/zxR9aoUYPm5uYcNGgQz58/r7yH165dy7NF1snJSRlkV65cWWCQfbtAwaBBg2hpaUk9PT0GBAQoW8P27NlDa2tralXTotorNaIHCHsQ+iAqgdiZR0id/KZ1cG/O+7dL9L516dIlOjo60tzcnBYWFuzfvz8PHTqU5wT0crmcd+7c4bZt2zhr1ixWq1aNGhoa1NHRYZkyZdi4cWOOHDmy2IGdJPfu3cvy5cvnat1/34MHD2hsbFzgMW/vo7q6OrOyspiWlsaHDx/yxo0bPHv2LA8ePMgNGzYwMjKSY8aMod0Fu8ID64dfSW/uafK7bU3YpMiftSgyMzPZo0cPfvXVVyrL4xaHQqHgihUraGlpyW+++aZIizQIgiAIOURgLYFev37NatWqcciQIVQoFNyzZw/Nzc1pamrK69ev5zo+ISGBpqam3LBhAytUqMCuXbvmW3bdunVVWmezs7NZpkwZzpgxgyYmJpw8eTIT0hJoONWQ0g1S2l+wp+1+W9ottOOgaYOU58lkMtauXZtqamo0NDRUKbMgjx494sCBA2lqasqpU6fywoUL7N69O01MTNipUyf26tWLVlZWrFixIqdOnco///xTea5cLueBAwfYpUsXlipVis2aNePmzZuZmZlZpGu/H2SnTJlSaJBNS0tTuU+//fYbra2tqampyYCAAC5evJiJiYksf7k88QrExDePq+Ugtr0JrnfeC1Y3QXiAsHoXWCWUsGFaQ0ZERNDHx4fq6up0d3fnnj17lEHwbavurl27lKs/+fn5UV9fn7a2tgwODubQoUP5008/ceLEibSysuK1a9eKdE/yo1AoGBgYyJ9//rnA47y9vXn48OFCyzM0NCzScq4d2bH4gfXhm8D68t22ziz6qm+FefbsGWvVqsVmzZopu+sUV3x8PAMDA+nt7c3Tp08XfoIgCIKgQgTWEurZs2d0d3fnnDlzmJmZSRMTE1aqVCnfFswqVaowJiaGenp6dHd3z7dcCwsL3r9/X/l+69atrFy5Mh0dHWliYsKUlBRev36dRkZGlEqlLFu2LBs3bsyOHTsqr52SkkJnZ2dqaGjQ1taWV65cKfTzJCcnc9KkSTQxMeGgQYN45MgRduzYkWZmZpw0aZLKkqVyuZyHDx/mwIEDaWVlRVdXV9auXZs2Njb09PTkvHnzPus66oUF2WbNmimD7MmTJ9mpUyeamZkxICCA+o76VMvMpyuAJ4gN770PBrEDhMO7wAqCSAc9anvQ0NCQY8eO5d69e7lgwQL27t2bVatWZalSpWhpacmgoCB+++23/P7773ns2DG+ePEiz8/z008/0dbWljdu3Pik+3L48GGWKVOmwF8Ixo0bx5EjRxZalpWVFf/6669Cj5vN2dShTs59keXcG4wC0enN9zIQf4C49uYXg6cg2oKo9e5+6lKXczinWJ81P9evX2e5cuWUy8YWV1paGseNG0dTU1NGRER8VBmCIAiCCKwlWmJiIu3t7dm7d2/WqVOH/v7+nDdvXp7Hvu0W4OHhQW1t7Twf/yYlJdHExEQl9NatW5cDBgygoaEhFy1axKysLFauXJnDhg2jVCqlk5MT69Spowwtd+/epbGxMbW0tFitWjXloKD8ZGZmctGiRSxdujQ7duzIHTt2sHXr1rSwsOCMGTPyfSyakpLCFStWsGbNmixVqhQ9PT1pamrKihUrcvr06Xm2NH9uMpmM8fHxyiAbGhqqDLLW1tYsVaoU9SfrU5Kex4C1hyC0QcS/eR8Notmb7z8MrK9B3Qm6NDIyoqmpKWvWrMn+/ftz8eLF/P333/nkyZNi133ZsmW0s7PjrVu3PukeBAcHc+nSpfnuP3HiBD09PQstx9nZuUg/s0d89C6wTnzTcvr+ayKINSDKgJCCKA2i85tuAW++dKjDx3xcrM+Zl4MHD9LS0pLLly//qPNjY2Pp5OTEVq1aqfySKAiCIBSfCKwl3JUrV6ihocFJkybx9u3bNDc3z/OR4ttuAf369aONjQ2PHz+e65jdu3ezdu3ayvdXr16lpaUl7ezsaG1tzaysLE6cOJGBgYG0s7Nj27ZtqaampmypO3bsGLW0tKilpcXevXsXOPpeLpdz3bp1dHJyYnBwMFeuXMlmzZrRysqK4eHhec5koFAoeOjQIXbr1o1GRkZs0qQJN2zYoBwUlJ2dzd9//50DBgygpaUlv/rqK86YMeOTWxKL622QjY6OZpkjZXKH1SwQQSC+fvM+GYQz3nUP+DCwEqz7oC4fPnz4WQfgLFmyhA4ODrxz585Hl3H69Gna2NiodI94X3Z2Ns3MzAocHEeSFStWVPYxLkwLtqBEUbxZK95+SShhS+YeyFZcP/74Iy0sLLhv375in5uUlMT27duzTJky3L59+yfXRRAEQRCBtcS7ceMGzczMaGZmxuPHjzM6OppOTk55tkxWqVIlZ+CKnR0jIyNz7Z8zZw4HDXrXD3XAgAEMCQmhnp4e165dyxMnTtDc3JwBAQEMCQmhnZ0dmzZtyp9++omrVq1SjvBetGhRgXXet28ffXx86OPjw/nz5zM4OJi2traMjIzMM/jcvXuXkydPpqOjI93d3U6TbUkAACAASURBVBkWFsakpKQCr5Gdnc2DBw+yf//+tLS0pJeXF2fOnMmbN28WeN7n1oRNVCOTHEQoiIZvgitBDEXOYKu3X3kE1s89SOithQsXsmzZsoUGyoKEhIRw/vz5+e7v1KkTo6KiCiwjICCAR48eLdL1TvEUtbO1PyqwfjiIrbjkcjm/++47Ojk5MT4+vtjnLlmyhGZmZhw5cmSB08sJgiAIxSMCawm3evVqtmrVijt37qSFhQWvXLnCPn36sEOHDrla48LDw9m+fXvq6uqyS5cuucrq2LEjf/rpJ5I5UycZGxvT0tKS5cqV46tXr5QDj/z8/GhmZsZLly5xwYIF9PHxoUQioVQq5f79+/Ot67lz51i/fn06Ojpy/PjxrFWrFsuWLctly5bl6geZmprKX3/9lXXq1KGpqSkHDBjA06dPf1QLY3Z2Ng8cOMB+/frRwsKC3t7e/1h4VRkkpEDOnKC1QKS9F6MqgTAFYfnmpQbCGMSsd8e4n3X/6AE9hZk3bx6dnJw++rH0pUuXWLp06Xzrt2bNGjZt2rTAMurWrcvY2NgiXzNwbWDONGvFDKvvT7dWXKmpqWzRogVr1KhR7G4YFy5coL+/P6tWrcpLly59dB0EQRCEvInAWsJ99913nDZtGsmcOVTt7e15/fp1enh45BrB/bZbgL29PZ2dnXOV5enpqZw/MiIigpUrV6aOjg5jY2PZu3dv1qxZk7a2tjQ1NeX+/fupUChYq1YtAmDp0qXzffR+69YtdujQgZaWluzfvz8DAgJYvnx5rlixQqXbgEKh4JEjR9izZ08aGRmxUaNGjI6OZnp6+me6W+/Ca9++fWlhYUEfHx/OmjXrk/ty5kdlkFAfEP7ImSf0/a+nyOlj+fZli5w+rW+O05Hr0HuNN21tbblmzZq/ZV7OOXPmsHz58oVOF5afdu3a5ZoO7a1nz57RwMCgwJ9j8+bNuXnz5iJdS6FQ0NbWlhMeTKCUUkpYcPcACSWfHFb/+usv+vj4sGvXrgXOI/yhlJQUDhs2jObm5ly2bFme048JgiAIn04E1hJuyJAhDA8PV76fO3cu3dzceOzYMZqZmeV6bFmlShUGBwdTU1NT5fF7RkYGdXR0mJ6eTrlcznLlytHY2Jje3t7cunUrbWxsaGxszNKlS3PVqlXMzMykl5cXAVAqleY5JdHjx4+Vk/y3b9+e3t7edHd359q1a1VGQyckJHDatGl0dnamq6srZ8+eXaQR459KJpNx//797NOnD83Nzenj48PZs2d/1lWFlIOE7iJnUJA2CL33XqvyiFgOql0C3g4SOnr0KL28vFijRg2VVas+l+nTp9PV1fWjZli4du0azczM8p2ZoFq1aty9e3e+53fo0IGrVq0q0rXOnDnDcuXKUaFQ8DRPsyVbUoc61KWuyn3UpS51qMOWbPlJ3QDOnTtHOzs7zpgxo1i/LGzZsoX29vbs3LkzHz169NHXFwRBEAonAmsJN2TIEIaFhalsGzZsGKtWrcrIyEhWrFhRpWUrPDycgYGBLFWqFE+cOKHcfv78ebq5uZHMGXxla2tLTU1N7t69mxYWFrSysqK9vT1nzpzJZ8+e0dLSkhKJhE2aNOGHP+vU1FROnTqVJiYmbNCgAd3c3Ojl5cVNmzYpW5hev37N1atXs27dujQxMWHfvn3zXaXpnyCTybhv3z5lePX19eWcOXM+aUDSWy3YotBWwHy/skH70/a8e/cuyZwW4qioKOWiAe9P9/U5TJ48mW5ubh8VsLp166ay6tr7ZsyYwW+//Tbfc3v37s3vv/++SNcZP348hw8frrLtMR9zDuewMzuzCZuwMztzDud88mwAW7dupbm5OWNiYop8zr1799i8eXOWL1++wC4ygiAIwucjAmsJFx4ezgEDBqhsk8vl7Ny5Mxs3bszWrVur7E9ISKCxsTG1tbVVBl69vyRro0aNqKenx8DAQDZs2JCOjo60s7Njv379ePXqVeWa8IsWLeK6devYvHlzkjlLdC5ZsoSlS5emv78/nZyc6O/vz+3bt1OhUFChUPDYsWPs3bs3jY2NGRwczHXr1n3WR/6fg0wm4969e/n111/TzMyMfn5+nDt37keH11M8RSmlHxVYdRW67LGkh3J+2rdB8tmzZ+zfvz8tLCwYFRX1WefvHDduHD09PYvdT/P27ds0MTHJ87wLFy7Qyckp319IBg8enO+UbB/y9PTkkSNHilW34lIoFAwLC6O1tTVPnTpVpHNkMhnDw8NpamrKyZMnF6vrgCAIgvBpCgqsahD+dZUrV8bJkydVtqmpqeHHH3+EQqGAlpYWduzYgc2bNwMA7O3t4eLiAm1tbezfv195zsWLF1GpUiXcunULhw4dQmZmJmrXro3z588jNTUVlSpVQtOmTeHh4YHs7GwcOHAAAwYMwKlTp1C5cmXExMTAzc0NkZGR0NDQgLa2NqKionDixAlUqlQJs2bNgqurK7p37w5HR0dcunQJu3fvRmhoKHR0dP7Re1YYDQ0N1K1bF99//z2SkpIwffp0XL9+HX5+fvD390d4eDgSEhKKXJ4f/BCGMEghLVY9pJAiXBKOH/v9iKtXr0KhUKBChQqYNGkSNDQ0sHjxYsTGxmL16tWoXLkyjh8/XtyPmqcpU6agcePGqFevHp4/f17k88qWLYu2bdti7ty5ufZVrFgRGRkZuHHjRp7nSqVSvH79utBr3LlzBw8fPkRAQECR61VcMpkMffr0wa+//ooTJ07Az8+v0HNOnjwJX19f7Ny5EydOnMCECROgra39t9VREARBKIb8kixFC+s/Ji0tjUZGRnmuX56amsrKlSuzS5cutLCwUD5WDg8PV86t+tbbJVkHDRpEbW1t1qlThwYGBtTX16eXlxfDwsIIgEZGRspy5HI57ezs6OLiQltbW1pYWLBevXo8dOgQ09LSuHbtWgYHB9PY2Jhff/01jx8//q898v8csrKyGBsby169etHU1JT+/v4MCwsr8pRQS7jkkwcJ3b59W/nznDdvHtPT06lQKLhmzRra2NiwU6dOHz1w6n0KhYLDhg2jj49Pvv1S85KYmEgTE5M8px3r3bt3vq2o06dP56hRowotf/78+ezRo0eR61Ncz58/Z1BQEBs3bszk5ORCj3/x4gX79eun7Nv9Jf/9FgRB+JJBdAko+QYNGsTvvvsuz31Pnjyhi4sLmzZtyqpVq1ImkzEhIYHSMlJiBNhO1o5NFE2oHa3N4Y+HU8dOh+rq6nRxcaG2trYyBAGgm5ubcu7ICxcu8KuvvqK6urpyRP/x48f5xx9/sG/fvjQxMWG9evW4evXqPFfV+tJlZWVxz5497NmzpzK8hoeHFxpeP9cgobi4ODZr1oz29vb88ccfKZPJmJKSwlGjRtHU1FS5ZO+nUCgUHDRoECtXrpznoLr8DB48OM/+qlu2bGHdunXzPGfBggX85ptvCi27Vq1a3Lp1a5HrUhw3b96ki4sLhwwZUmgXC4VCwbVr19LKyop9+vTh8+fP/5Y6CYIgCEUjAusX4Pbt2zQ1Nc2zlZXMmXjf1taWnp6e7L6ke84goAwJ8Vq1ZU8zS5NIAw1iDahVTYv6+vr08fEhALZq1YpyuZx37txhu3btqKenRzU1Nfr4+HDPnj2cNWsWXV1d6ezszGnTpvHevXv/8F3492RlZXH37t3s0aMHTU1NWaVKFc6bN6/Ae/C5BgkdP36cNWvWpKurKzdu3EiFQsHr16+zUaNGdHFxKXBkflEoFAoOGDCAAQEBRWpxJMlHjx7RxMQk1+dPSUmhgYFBrnJSU1M5ZcoUhoSEcP/+/YyLi2NiYmKu1sqnT5/S0NAw31W1PsXhw4dpaWlZ4DKzb924cYP169enp6dnnivGCYIgCP88EVi/ENOnT2fNmjXzbRmKi4uj3jA94jUokRcyYj0bRCqoP1yfADh9+nQ+efKE/fr1o66uLqVSKcuXL08XFxc2aNCARkZG7NmzJ48ePfp//0g0KyuLu3btYo8eOQOlAgICOH/+/Hx/mfgcFAoFd+3aRS8vL/r5+SmXC922bRudnJzYvHnzT5pnVi6Xs0+fPqxevXqRFy8YPXo0v/7661zb69aty7CwMI4aNYr169envb09dXV1Wbp0aWpqahIA3d3dWbp0aeUvTJ06deKvv/7KqKgohoSEfPTnyM8vv/xCCwuLQhcuyMjI4NSpU5Ut2AUtPSwIgiD8s0Rg/UJkZ2czKCiI/fv3z3Ny8iVcUvzlLFPBr899zTFjxlBXV5c6OjqsW7cuAwICKJFIWL16da5cuVIsMZmPzMxM7ty5k927d6eJiQmrVq3KBQsWfPSKUoWRy+Vct24dnZ2dWbduXZ46dYrp6emcPn06TU1NOX78+I/uniGXy9mzZ08GBgYW6ef97NkzmpqaKlcTS01N5fTp02lubk4DAwOOGTOG27Zt482bN5mdnc2tW7fmWg3rxYsXPHHiBJcvX84mTZpQQ0ODvr6+PHny5Ed9hrw+09ixY+no6MgrV64UeOzvv/9OV1dXNmnSRNmHWxAEQSg5RGD9grx48YKBgYFs27atypQ6ymmVIkH4gNAC0fW9YHoCRF3kLAdqBqI1iAfvQqu6vzorVapEZ2dnmpiYsFSpUtyzZ8+/+Em/PG/Da7du3WhsbMxq1ar9beE1KyuL33//PW1sbNiqVSvGx8czMTGR7dq1o729PaOjoz+qJVwul7Nr166sU6dOkYLv5MmT2blzZ65Zs4a2trZs3749Y2JiaGVllev6+/btY506dfItKy0tjQYGBpw9ezatrKzYtWvXTxpc9vr1a7Zu3ZrVqlXj48f5d8N48uQJu3btSltbW27atOn//gmCIAhCSSUC6xcmPT2dbdu2paenJw8cOEDyvYnrN4LYDKLvB4F1J3KWAn2FnH6t3UEEv+seoLFVg61atWLVqlXp4eHxf9U/9e+QmZnJHTt2sGvXrjQ2Nmb16tUZERHx2Vf3ev36NWfPnk0zMzP27NmT9+7d4++//05PT0/WqVOHly9fLnaZ2dnZ7NixI+vVq1fo/LmPHz+mtrY2XV1defToUeV2Z2dnnj9/XuXY48eP09/fP9+ytm3bxpo1a5Ikk5OTOXLkSFpaWqqUW1RJSUn08/Njx44d8/0McrmcP/74Iy0sLDh48OAi998VBEEQ/h0isH6BFAoFY2Ji6ODgwEbdGlEzW1P1Uf/YDwLrh19nQei/e6+epU7j8sacNWuWmAz9M8vIyOD27dvZpUsXZXhduHDhZw2vL1684JgxY2hiYsKhQ4cyKSmJkZGRNDc356BBg4o1bRWZM0F+aGgoGzZsmO/fh5cvX7J27dr08PBgy5YtVfYNGjSI06dPV9l28eJFenh45HvNXr16qSxBTOasyGZubs7NmzcXue4XL16kvb09p0yZkm9r6ZUrV1ijRg36+vry7NmzRS5bEARB+PcUFFjFwgEllEQiQevWrREfHw+1HmrIlmUXr4DDANzfK48SDDg1ACNHjhSToX9m2traaNy4MX755RckJSVh5MiROHPmDNzd3VGzZk0sWrQISUlJn3QNIyMjTJ8+HZcvX0ZGRgY8PDzw/PlznDp1Cunp6ahQoQJ++uknKBSKIpWnoaGBVatWQSqVonXr1sjKylLZTxJdunSBo6Mjjh07huPHj+PChQvK/Y0aNcKOHTtUztHT08t34QCFQoFt27ahefPmKtuDg4Oxc+dO9O7dG+fOnSu03jt27EDdunUxZ84cjB8/HhKJRGV/Wloaxo4di8DAQISGhuKPP/6At7d3oeUKgiAIJZsIrCWcrq4uStUoBeqw6CddAjAFwHuLFWVrZSOhVNFXdhI+jra2Npo0aYJffvkFDx8+xIgRI3Dq1Cm4ubkhMDAQixcvxsOHDz+6fCsrKyxevBgnT57E9evXUaVKFbi7u2PTpk344YcfUKVKFZw6dapIZWloaGDt2rXQ0NBAaGgoZDKZct/ixYvx4MEDLFmyBIaGhhg9ejQmTJig3B8YGIjLly/j2bNnym16enpIS0vL81onT56EmZkZnJyccu3z9fXFokWL0K5dO6SkpOR5PklERESgd+/e+O233xAaGprrmN27d8PT0xM3b97ExYsXMWDAAKirqxfpXgiCIAglmySnBTZvvr6+PHPmzD9YHSEvTdEU27FddeM4APcBrPjg4JsAAgHMAtBZdZfRUSM0WNwA9vb2cHBwUPmzVKlSf1PtBQDIyMhAbGwsYmJisH37dlSqVAlt2rRBq1atULp06Y8u99KlSxg7dizi4uIwadIkAMCYMWPQoEEDzJw5E5aWloWWkZWVhVatWkFHRwdr167Fy5cvUb58eZw6dQrOzs7K+pcrVw4bNmyAv78/AKB58+YIDQ1Fhw4dAAApKSmwtrbOM3SOHDkSmpqamDZtWr716NKlC+zt7XMdk52djW+//RZHjhzBtm3bUKZMGZX9Dx48wJAhQ3D69GksWbIEDRo0KPQzC4IgCCWPRCI5S9I3z30isJZ8ndAJq7FadWNegTUBOWF1FIC+ucsJfhSMzvs64969e0hISFD+mZCQAA0NDWWA/TDMOjg4oHTp0lBTEw3yn8Pb8BodHY3t27fDy8tLGV6LEjDzcvToUYwePRovXrzA2LFjcebMGfz6668YO3YsBgwYAE1NzQLPz8zMRIsWLVCqVClUqFABCQkJ+PHHH1WOWbZsGTZs2IDY2Fjl+8OHD2PVqlUAALlcDi0tLWRnZ+d6VO/q6oqVK1fCz88v3zrcuHEDVatWRUJCAqRSKQDg5cuXCA0NhZqaGtavXw9DQ0Pl8XK5HEuXLsXkyZPx9ddfY+zYscrzBEEQhC+PCKxfuDmYg4mYiAxkANnIeU1GTmBdDkADwCMANQH0AzA8dxm60MVkTMYIjMi1jyRevHihEmI//PPFixewtbXNN9Ta29tDR0fn77sJ/1EZGRnYs2cPoqOjsWPHDnh5eaFt27Zo2bJlscMrSezatQujR4+GVCpF3759sXr1ajx48AALFy5EnTp1Cq1L06ZNcezYMZw4cQKVKlVS2S+TyeDq6oqffvoJgYGBSExMhJeXFx49eqR89K6rq4vnz59DV1dXed61a9cQFBSExMTEQn/padasGVq2bIlu3brh9u3baNKkCYKCgjB//nxoaGgojzt37hz69u0LHR0dREVFwc3NrVj3ShAEQSh5RGD9wj3GYzjAISewTkJOWH3fRAAS5OzT+2Bfas4fOtDBPdyDOcw/qg4ZGRlITEzMN9Tev38fpUqVUgmyH4ZaExOTXC1vwjvp6enYs2cPYmJisGPHDnh7eyvDq4WFRZHLUSgUWLduHcaPHw9nZ2fUr18fixYtgq+vL8LDw2Fvb5/vuVeuXIGPjw/atWuHn376KVfAXLlyJZYvX45Dhw5BIpGgUqVKiIqKQkBAAADAzMwM165dg5mZmfKc2bNn4+7du1i6dGmhdV+yZAnOnTuH7t27o3Xr1hg7diwGDhyo3J+SkoIJEyZgzZo1mDlzJrp16yZa/gVBEP4jRGD9D2iJltiCLSCKMfjqDQkkaIEW2IiNf0PNcigUCjx8+DDfFtp79+5BJpPl6mrw/vfW1tYqrWj/z9LT07F7927ExMRg586d8PHxUYZXc/Oi/dKRlZWFH374AdOmTUNAQABsbGywZs0aDBo0CCNGjMizRXzdunVYs2YNXr16hXLlymHZsmUqgVAul8PDwwMRERGoX78+Ro8eDXV1dWW/U3t7exw5cgQODg7Kc6pWrYqJEyciODi40DqfOXMGLVq0QGZmJn799Vdlf1SS2Lx5MwYNGoS6deti7ty5KqFYEARB+PKJwPofcBqnUQu1kIa8R2EXRAopDuEQfJHn34F/THJycoHdDh4/fgwrK6s8B4W9/VNfX/9f/Qz/hvT0dOzatQsxMTHYtWsXfH190bZtW7Ro0aJI4fX169dYuHAh5s2bh3r16uHVq1eIj4/HvHnz0Lx5c5VW74iICNy6dQszZsxAgwYN4OHhgaVLl6ocExMTg7lz5+LkyZM4duwYvv32W+WUVBUqVMDGjRuVj+gfPXoEFxcXPHr0qNDp1Ehi+PDhWLBgAS5evAgPDw8AQEJCAgYOHIibN28iKioKgYGBxb6HgiAIQsknAut/xFIsxXAML1ZolUKKMIShH/r9jTX7PGQyGe7fv59vqL137x50dXXzDbMODg6wsLD4T3c7SEtLw+7duxEdHY1du3ahcuXKaNOmTZHC6/PnzzFnzhwsX74cQUFBuHjxIsqUKYOIiAi4uroCyAmst2/fRkREBFJSUlC/fn34+PggMjJSeV8VCgW8vb0xZcoUNGrUCJaWloiLi4O1tTV8fX0RFRUFX9+cf29++OEH7N27F+vXry+wbhkZGejevTtu3LiBu3fv4unTp5DJZFiwYAFmz56NwYMHY8SIEWIOYUEQhP+wggKreP76BXkbOodjONKRXmD3AAkk0IXuFxNWAUBTUxNly5ZF2bJl89xPEk+fPs01w8GxY8eU21JTU2FnZ5dnmHVwcICtrS20tLT+4U/2+UilUrRs2RItW7ZEWloadu3ahejoaIwYMQL+/v7K8JrX43ITExPMmjUL33zzDaZOnYpnz57ByckJ1atXR/fu3TF+/Hjo6OggNTWn47OBgQF2796NevXqYciQIZg/fz4kEgnU1NQwdepUjBs3Dk2aNEH9+vWxa9cu9OzZM9fiAVu2bFFOe5WfR48eISQkBGXKlFF2Azh+/Dj69u0LKysr/PHHH8rptQRBEIT/T6KF9Qt0BmcwEzOxEzshgQTpSFfu04UuCKIRGmE0Rv/r3QD+aa9fv841OOz975OSkmBmZpZvC62Dg8MXOSdtWloadu7ciejoaOzZswf+/v7KbgOmpqZ5nnPjxg1MmDAB+/fvh5OTE+7du4fevXtj69atOH/+vPK4ly9fIigoCHXq1MGcOXMgkUhAElWqVMHQoUORlZWFzZs3Y9OmTWjUqBEGDhyIRo0aITU1FdbW1rh37x6MjIzyrMPly5fRtGlTdOnSBZMmTcKKFSswefJkZGVlYd68eQgNDf1Pt5gLgiAI74guAf9RT/AEK7ACcYjDC7yAMYzhCU90Q7ePng3gv04ul+PBgwd5zkX79ns1NbUCux1YWVmV6JHpr1+/VobX2NhYVKlSBW3btkVISEie4fX8+fMYM2YMLl68CB0dHSQkJODAgQMqfUWfP3+OoKAgNGjQADNmzIBEIsHevXvxzTff4ODBg6hQoQIeP36Mjh07om3btmjTpg02bdqEpUuXYu/evXnWc/fu3ejSpQvmz5+PDh06YM2aNfj666/h4eGBPXv25BtyBUEQhP8mEVgFoYhI4uXLl3n2o337/fPnz2FjY5NvqLW3t1eZh/Tf9Pr1a+zYsQMxMTGIjY1FQECAMryamJioHHvo0CGMGjUKFy9eBEl07twZM2bMUHYvePr0KerUqYOQkBBMmTIFJFGrVi306NEDC9cthPdCbxx4cgAGtgbwsPdA/Lp4tE5tjdG9Rueq16JFizB9+nRs3LgRZmZm6N+/Px49eoS//vpLZYUtQRAE4f+HCKyC8BllZGTkOzgsISEB9+/fh6GhYb6rhtnb28PU1PQff9SdmpqqDK979+5F1apV0aZNG5XwShLh4eEYM2YMTE1NkZGRgWnTpqFPnz7Q0NDA48ePUbt2bYSGhmLChAlYdn4Zhjwegsw6mQABuZb83QXTAG1dbTSS5HRP8YMfsrOzMWTIEBw4cAAbN27E+vXrERkZiTFjxkBDQwMHDx7E5s2b/9H7IgiCIJQMIrAKwj9IoVDg0aNH+U7flZCQgKysrAKXwrWxsflb56R9G16jo6Oxb98+VKtWTRlejY2N0axZMygUCpw/fx4ZGRkwNTXFDz/8gJo1a+Lhw4eoVasWXOa7YF/DfUhTpAEF9JB4OwBwWvo07G21F3K5HAMGDMCIESPg5uaGiIgIKBQKVK5cGXv27IGXl9ff9rkFQRCEkksEVkEoYZKTk5VTdeUVah89eoTSpUsXGGo/15y0qamp2L59O6Kjo7F//35Uq1YNDRs2xKxZs7B06VIkJCRg/PjxkMlkqFOnDpYuXYpV+qswRnsMqFv0hSwk6RL4R/vDMdYRR48eRWRkJJo1a4bMzEzUqlULrVu3xrBhwz7LZxIEQRC+PCKwCsIXRiaT4a+//ipwoQUdHZ0Cl8K1sLAo9uCwlJQUZXiNjY1FdnY2+vTpg++++w7Lli1DWFgY5N5yyHfJIR8sB/YBeA7ACcBMAA3fFBSNnCWD7wOwAzADQAiANKDjso6I6hUFfX19vHr1CiEhIbC0tMSaNWtK9GA2QRAE4e8lAqsg/Me8nZO2oKVwk5OTc81J+/73tra2BU7En5KSgoULF2LatGkgiTp16qBJkyaYVXkWEssnAvMAdANgD2AngPYA4gBoAigLYCuABm/2tQFwF5CYS9BCkrNM8OXLl9GhQwcEBgZiwYIFUFdX/1vvmSAIglCyicAqCP+H0tLSCux28ODBA5iamhY6J+3du3fRs2dPXL9+HVaVrHBm4xlAJ48LVkROq6otgKYAHr+3zxzAbwACAG1qo9OYTvjtx98wZcoU9OnTR8y1KgiCIIiVrgTh/5FUKoWrq6ty2dUPyeVyJCUlqQwGu3r1Knbt2qXcBgAODg6ws7ODp6cnDvkdQp4LrD0CcB2AO4ByACogJ6A2BrANgDZyAi2AzPRMXPG7gqvDrua5IpcgCIIgfEgEVkH4P6Wurg5bW1vY2tqiatWqufaTxKtXr1QXVghIQLxuvOqBMgAdAXQF8DYbdwHQAUAGAC0AMQD03uyTAuValoMZRFgVBEEQikYEVkEQ8iSRSGBkZAQjIyNUqlQJALAHexCP9wKrAkBn5ITSRW+27QPwHYDfAXgDOAugGYBdAL7KOeQFXvwDn0AQBEH4rxBDcgVBKLJSKPXuLaVklwAAAppJREFUDQH0RE53gI3IGWwFABcA1ATgi5x/YfwA+CMnyL5hDON/oLaCIAjCf4UIrIIgFFlFVITO2xFX/QDEI6eP6vsr0foBOIKc4AoA59+8f9OHVRe68ITnP1JfQRAE4b9BBFZBEIqsG7rlfJMA4HvkhNLSAPTfvFYDCAQwCUBrAAYAWgEYA6B+zqkE35UjCIIgCEUg+rAKglBkFrBAQzTEFoctKGhKPAx88/qABBI0QiOYw/xvq6MgCILw3yNaWAVBKJbRGA1dlT4ARacLXYzG6M9cI0EQBOG/TgRWQRCKxQ9+CEMYpJAW6zwppAhDGHyR55zQgiAIgpAv0SVAEIRi64d+AIDhGI50pIN5riaQQwIJdKGLMIQpzxMEQRCE4hAtrIIgfJR+6IdDOIQWaAEd6OTqJqALXehABy3QAodwSIRVQRAE4aOJFlZBED6aL3yxERvxBE+wAisQhzi8wAsYwxie8EQ3dBMDrARBEIRPJgKrIAifzBzmGIER/3Y1BEEQhP8o0SVAEARBEARBKNFEYBUEQRAEQRBKNBFYBUEQBEEQhBJNBFZBEARBEAShRBOBVRAEQRAEQSjRRGAVBEEQBEEQSjQRWAVBEARBEIQSTQRWQRAEQRAEoUQTgVUQBEEQBEEo0URgFQRBEARBEEo0EVgFQRAEQRCEEk0EVkEQBEEQBKFEE4FVEARBEARBKNFEYBUEQRAEQRBKNBFYBUEQBEEQhBJNBFZBEARBEAShRBOBVRAEQRAEQSjRJCTz3ymRPAGQ8M9VRxAEQRAEQfg/5UDSPK8dBQZWQRAEQRAEQfi3iS4BgiAIgiAIQokmAqsgCIIgCIJQoonAKgiCIAiCIJRoIrAKgiAIgiAIJZoIrIIgCIIgCEKJ9j/5/eHg1RjLqQAAAABJRU5ErkJggg==\n","text/plain":["
"]},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"86QL4d9vzKqM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638377051252,"user_tz":-60,"elapsed":42,"user":{"displayName":"Gian Carlo Milanese","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08352423327035271918"}},"outputId":"96a4c70b-6652-470c-99ff-03e52ad94586"},"source":["scores = networkx.pagerank(similarity_graph)\n","ranked_sentences = sorted(((score, index) for index, score\n"," in scores.items()),\n"," reverse=True)\n","ranked_sentences[:10]"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n"]},{"output_type":"execute_result","data":{"text/plain":["[(0.040721209316877505, 22),\n"," (0.03994787372328047, 9),\n"," (0.03939359424138909, 10),\n"," (0.0375833609071944, 16),\n"," (0.03744652515777444, 27),\n"," (0.037387204944521565, 18),\n"," (0.03661709582201522, 1),\n"," (0.03563095404425751, 11),\n"," (0.035492104383262474, 20),\n"," (0.03518843307678377, 0)]"]},"metadata":{},"execution_count":78}]},{"cell_type":"code","metadata":{"id":"Zvc_yHYhzM37"},"source":["num_sentences = 5\n","\n","top_sentence_indices = [ranked_sentences[index][1]\n"," for index in range(num_sentences)]\n","top_sentence_indices.sort()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bbdn1FRBzaWq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638377051254,"user_tz":-60,"elapsed":25,"user":{"displayName":"Gian Carlo Milanese","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08352423327035271918"}},"outputId":"240a289c-8520-4bf2-cec7-7e62022991e2"},"source":["print('.'.join(np.array(sentences)[top_sentence_indices]))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["By the first century BC, the Roman Empire emerged as the dominant power in the Mediterranean Basin and became a leading cultural, political and religious centre, inaugurating the Pax Romana, a period of more than years during which Italy's law, technology, economy, art, and literature developed..During the Early Middle Ages, Italy endured the fall of the Western Roman Empire and barbarian invasions, but by the th century numerous rival city-states and maritime republics, mainly in the northern and central regions of Italy, became prosperous through trade, commerce, and banking, laying the groundwork for modern capitalism..Centuries of foreign meddling and conquest, and the rivalry and infighting between the Italian city-states, such as the Italian Wars of the th and th centuries, left Italy politically fragmented, and it was further conquered and divided among multiple foreign European powers over the centuries..Following the rise of the Italian Resistance and the liberation of Italy, the country abolished its monarchy, established a democratic Republic, enjoyed a prolonged economic boom, and became a highly developed country..Italy is a founding and leading member of the European Union and a member of numerous international institutions, including the United Nations, NATO, the OECD, the Organization for Security and Co-operation in Europe, the World Trade Organization, the Group of Seven, the G , the Union for the Mediterranean, the Latin Union, the Council of Europe, Uniting for Consensus, the Schengen Area, and many more.\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fqDuHBHrpS3P","executionInfo":{"status":"ok","timestamp":1638377051255,"user_tz":-60,"elapsed":20,"user":{"displayName":"Gian Carlo Milanese","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08352423327035271918"}},"outputId":"04e66606-2bc2-45b7-caf5-3994004ec8c6"},"source":["print('.'.join(np.array(sentences)[top_sentence_indices]).replace('.', '.\\n'))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["By the first century BC, the Roman Empire emerged as the dominant power in the Mediterranean Basin and became a leading cultural, political and religious centre, inaugurating the Pax Romana, a period of more than years during which Italy's law, technology, economy, art, and literature developed.\n",".\n","During the Early Middle Ages, Italy endured the fall of the Western Roman Empire and barbarian invasions, but by the th century numerous rival city-states and maritime republics, mainly in the northern and central regions of Italy, became prosperous through trade, commerce, and banking, laying the groundwork for modern capitalism.\n",".\n","Centuries of foreign meddling and conquest, and the rivalry and infighting between the Italian city-states, such as the Italian Wars of the th and th centuries, left Italy politically fragmented, and it was further conquered and divided among multiple foreign European powers over the centuries.\n",".\n","Following the rise of the Italian Resistance and the liberation of Italy, the country abolished its monarchy, established a democratic Republic, enjoyed a prolonged economic boom, and became a highly developed country.\n",".\n","Italy is a founding and leading member of the European Union and a member of numerous international institutions, including the United Nations, NATO, the OECD, the Organization for Security and Co-operation in Europe, the World Trade Organization, the Group of Seven, the G , the Union for the Mediterranean, the Latin Union, the Council of Europe, Uniting for Consensus, the Schengen Area, and many more.\n","\n"]}]},{"cell_type":"markdown","metadata":{"id":"xuVsACiq4vox"},"source":["# Exercise 4"]},{"cell_type":"markdown","metadata":{"id":"CwHVlm-Wp7vX"},"source":["# Summarization\n","\n","- Use the Dataset used for topic modeling.\n","- Summarize all the paper using text rank algorithm.\n","\n","changes:\n","- Try to use different number of sentences.\n","- Try to use different settings for TFIDF"]},{"cell_type":"code","metadata":{"id":"yzhmDaOZrMKB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638377051257,"user_tz":-60,"elapsed":16,"user":{"displayName":"Gian Carlo Milanese","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08352423327035271918"}},"outputId":"9f5be419-a8d8-4beb-f010-0e2fc3f5cc8d"},"source":["import numpy as np\n","import pandas as pd\n","import nltk\n","nltk.download('punkt') # one time execution\n","nltk.download('stopwords')\n","import re"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data] Package punkt is already up-to-date!\n","[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data] Package stopwords is already up-to-date!\n"]}]},{"cell_type":"code","metadata":{"id":"A6Qyal1GrNdm","colab":{"base_uri":"https://localhost:8080/","height":276},"executionInfo":{"status":"ok","timestamp":1638377055717,"user_tz":-60,"elapsed":4471,"user":{"displayName":"Gian Carlo Milanese","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08352423327035271918"}},"outputId":"1ccf7c99-2a41-4e56-a030-66f651ec92f2"},"source":["!gdown 'https://drive.google.com/uc?id=1Ase_bvYHC_B4ngJu6fDxQ5jHrwM4mvul'\n","papers = pd.read_csv('/content/papers.csv')\n","papers = papers.drop(columns=['id', 'event_type', 'pdf_name'], axis=1).sample(100)\n","# Print out the first rows of papers\n","papers.head()\n","\n","### Answer here"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading...\n","From: https://drive.google.com/uc?id=1Ase_bvYHC_B4ngJu6fDxQ5jHrwM4mvul\n","To: /content/papers.csv\n","100% 184M/184M [00:01<00:00, 139MB/s]\n"]},{"output_type":"execute_result","data":{"text/html":["
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yeartitleabstractpaper_text
62761993Feature Densities are Required for Computing F...Abstract MissingFeature Densities are Required for\\nComputing ...
39302012A Polynomial-time Form of Robust RegressionDespite the variety of robust regression metho...A Polynomial-time Form of Robust Regression\\n?...
36482011Learning with the weighted trace-norm under ar...We provide rigorous guarantees on learning wit...Learning with the Weighted Trace-norm under\\nA...
56142016Truncated Variance Reduction: A Unified Approa...We present a new algorithm, truncated variance...Truncated Variance Reduction: A Unified Approa...
32412010Relaxed Clipping: A Global Training Method for...Robust regression and classification are often...Relaxed Clipping: A Global Training Method\\nfo...
\n","
"],"text/plain":[" year ... paper_text\n","6276 1993 ... Feature Densities are Required for\\nComputing ...\n","3930 2012 ... A Polynomial-time Form of Robust Regression\\n?...\n","3648 2011 ... Learning with the Weighted Trace-norm under\\nA...\n","5614 2016 ... Truncated Variance Reduction: A Unified Approa...\n","3241 2010 ... Relaxed Clipping: A Global Training Method\\nfo...\n","\n","[5 rows x 4 columns]"]},"metadata":{},"execution_count":83}]},{"cell_type":"code","metadata":{"id":"PjGI0xcZpijr"},"source":["# Solution"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"CjnjVB6E4nVc"},"source":["stop_words = nltk.corpus.stopwords.words('english')\n","normalize_corpus = np.vectorize(normalize_document)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F6VplEIU5Hh1","executionInfo":{"status":"ok","timestamp":1638378134475,"user_tz":-60,"elapsed":632,"user":{"displayName":"Gian Carlo Milanese","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08352423327035271918"}},"outputId":"08492cd1-ec56-4434-e4af-f3a831dfc163"},"source":["vectorizer = TfidfVectorizer(min_df=3, max_df=0.5)\n","vectorizer.fit(papers.paper_text)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["TfidfVectorizer()"]},"metadata":{},"execution_count":99}]},{"cell_type":"code","metadata":{"id":"iqTx7CgE4eso"},"source":["def summarize_doc(DOCUMENT, num_sentences=5):\n"," DOCUMENT = re.sub(r'\\n|\\r', ' ', DOCUMENT)\n"," DOCUMENT = re.sub(r' +', ' ', DOCUMENT)\n"," DOCUMENT = re.sub(r'\\d+', ' ', DOCUMENT)\n"," pattern = r'\\[[^\\]]*\\]'\n"," DOCUMENT = re.sub(pattern, ' ', DOCUMENT)\n"," DOCUMENT = DOCUMENT.strip()\n"," sentences = nltk.sent_tokenize(DOCUMENT)\n"," norm_sentences = normalize_corpus(sentences)\n"," norm_sentences[:3]\n"," dt_matrix = vectorizer.transform(norm_sentences)\n"," dt_matrix = dt_matrix.toarray()\n"," similarity_matrix = np.matmul(dt_matrix, dt_matrix.T)\n"," similarity_graph = networkx.from_numpy_array(similarity_matrix)\n"," scores = networkx.pagerank(similarity_graph)\n"," ranked_sentences = sorted(((score, index) for index, score in scores.items()),\n"," reverse=True)\n"," top_sentence_indices = [ranked_sentences[index][1]\n"," for index in range(num_sentences)]\n"," top_sentence_indices.sort()\n"," return '.'.join(np.array(sentences)[top_sentence_indices])\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gkX3AXxh8afg","executionInfo":{"status":"ok","timestamp":1638378214428,"user_tz":-60,"elapsed":40064,"user":{"displayName":"Gian Carlo Milanese","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08352423327035271918"}},"outputId":"6477b706-4f57-4186-a548-770881ac4e72"},"source":["\n","papers['summary'] = papers.paper_text.apply(summarize_doc)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n","/usr/local/lib/python3.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg.py:108: DeprecationWarning: networkx.pagerank_scipy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead.\n"," G, alpha, personalization, max_iter, tol, nstart, weight, dangling\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"41M0y_O04etN","executionInfo":{"status":"ok","timestamp":1638378214431,"user_tz":-60,"elapsed":29,"user":{"displayName":"Gian Carlo Milanese","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08352423327035271918"}},"outputId":"71caebca-91fa-43fa-c215-cc93df897568"},"source":["print(papers.paper_text.iloc[0])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Feature Densities are Required for\n","Computing Feature Correspondences\n","\n","Subutai Ahmad\n","Interval Research Corporation\n","1801-C Page Mill Road, Palo Alto, CA 94304\n","E-mail: ahmadCDinterval.com\n","\n","Abstract\n","The feature correspondence problem is a classic hurdle in visual\n","object-recognition concerned with determining the correct mapping\n","between the features measured from the image and the features expected by the model. In this paper we show that determining good\n","correspondences requires information about the joint probability\n","density over the image features. We propose \"likelihood based\n","correspondence matching\" as a general principle for selecting optimal correspondences. The approach is applicable to non-rigid\n","models, allows nonlinear perspective transformations, and can optimally deal with occlusions and missing features. Experiments\n","with rigid and non-rigid 3D hand gesture recognition support the\n","theory. The likelihood based techniques show almost no decrease\n","in classification performance when compared to performance with\n","perfect correspondence knowledge.\n","\n","1\n","\n","INTRODUCTION\n","\n","The ability to deal with missing information is crucial in model-based vision systems. The feature correspondence problem is an example where the correct mapping between image features and model features is unknown at recognition time.\n","For example, imagine a network trained to map fingertip locations to hand gestures.\n","Given features extracted from an image, it becomes important to determine which\n","features correspond to the thumb, to the index finger, etc. so we know which input\n","units to clamp with which numbers. Success at the correspondence matching step\n","\n","961\n","\n","\f962\n","\n","Ahmad\n","\n","/\n","\n","Class bouudary\n","\n","I\n","\n","Class 2\n","Class 1\n","\n","o\n","\n","?\n","\n","P2\n","\n","L -_ _ _ _ _ _ _ _~_ _ _ _ _ _ _ _ _ _ _ _ _ __\n","\n","o\n","\n","I\n","\n","Xl\n","\n","Figure 1: An example 2D feature space. Shaded regions denote high probability. Given measured values of 0.2 and 0.9, the points PI and P2 denote possible\n","instantiations but PI is much more likely.\n","\n","is vital for correct classification. There has been much previous work on this topic\n","(Connell and Brady 1987; Segen 1989; Huttenlocher and Ullman 1990; Pope and\n","Lowe 1993) but a general solution has eluded the vision community. In this paper\n","we propose a novel approach based on maximizing the probability of a set of models generating the given data. We show that neural networks trained to estimate\n","the joint density between image features can be successfully used to recover the\n","optimal correspondence. Unlike other techniques, the likelihood based approach is\n","applicable to non-rigid models, allows perspective 3D transformations, and includes\n","a principled method for dealing with occlusions and missing features.\n","\n","1.1\n","\n","A SIMPLE EXAMPLE\n","\n","Consider the idealized example depicted in Figure 1. The distribution of features is\n","highly non-uniform (this is typical of non-rigid objects). The classification boundary\n","is in general completely unrelated to the feature distribution. In this case, the\n","class (posterior) probability approaches 1 as feature Xl approaches 0, and 0 as it\n","approaches 1. Now suppose that two feature values 0.2 and 0.9 are measured from\n","an image. The task is to decide which value gets assigned to X I and which value\n","gets assigned to X2. A common strategy is to select the correspondence which gives\n","the maximal network output (i.e. maximal posterior probability). In this example\n","(and in general) such a strategy will pick point P2, the wrong correspondence. This\n","is because the classifer output represents the probability of a class given a specific\n","feature assignment and specific values. The correspondence problem however, is\n","something completely different: it deals with the probability of getting the feature\n","assignments and values in the first place.\n","\n","\fFeature Densities Are Required for Computing Feature Correspondences\n","\n","2\n","\n","LIKELIHOOD BASED CORRESPONDENCE\n","MATCHING\n","\n","We can formalize the intuitive arguments in the previous section. Let C denote the\n","set of classes under consideration. Let X denote the list of features measured from\n","the image with correspondences unknown. Let A be the set of assignments of the\n","measured values to the model features. Each assignment a E A reflects a particular\n","choice of feature correspondences. \\Ve consider two different problems: the task of\n","choosing the best assignment a and the task of classifying the object given X.\n","Selecting the best correspondence is equivalent to selecting the permutation that\n","maximizes p(aIX, C). This can be re-written as:\n","\n","C)p(aIC)\n","( I X C) = p(Xla,\n","pa- ,\n","p(XIC)\n","\n","(1)\n","\n","p(XIC) is a normalization factor that is constant across all a and can be ignored.\n","Let Xa denote a specific feature vector constructed by applying permutation a to\n","X. Then (1) is equivalent to maximizing:\n","\n","p(aIX, C)\n","\n","= p(xaIC)p(aIC)\n","\n","(2)\n","\n","p(aIC) denotes our prior knowledge about possible correspondences. (For example\n","the knowledge that edge features cannot be matched to color features.) When no\n","prior knowledge is available this term is constant. We denote the assignment that\n","maximizes (2) the maximum likelihood correspondence match. Such a correspondence maximizes the probability that a set of visual models generated a given set\n","of image features and will be the optimal correspondence in a Bayesian sense.\n","\n","2.1\n","\n","CLASSIFICATION\n","\n","In addition to computing correspondences, we would like to classify a model from\n","the measured image features, i.e. compute p( CdX, C). The maximal-output based\n","solution is equivalent to selecting the class Ci that maximizes p(Cilxa, C) over all\n","assignments a and all classes Ci. It is easy to see that the optimal strategy is actually\n","to compute the following weighted estimate over all candidate assignments:\n","\n","p\n","\n","(C.IX C) = Lap(CiIX, a, C)p(Xla, C)p(aIC)\n",", ,\n","p(XIC)\n","\n","(3)\n","\n","Classification based on (3) is equivalent to selecting the class that maximizes:\n","\n","(4)\n","a\n","\n","Note that the network output based solution represents quite a degraded estimate\n","of (4). It does not consider the input density nor perform a weighting over possible\n","\n","963\n","\n","\f964\n","\n","Ahmad\n","\n","correspondences. A reasonable approximation is to select the maximum likelihood\n","correspondence according to (2) and then use this feature vector in the classification\n","network. This is suboptimal since the weighting is not done but in our experience\n","it yields results that are very close to those obtained with (4).\n","\n","3\n","\n","COMPUTING CORRESPONDENCES WITH GBF\n","NETWORKS\n","\n","In order to compute (2) and (4) we consider networks of normalized Gaussian basis\n","functions (GBF networks). The i'th output unit is computed as:\n","\n","(5)\n","with:\n","\n","Here each basis function j is characterized by a mean vector f.lj and by oJ, a vector\n","representing the diagonal covariance matrix. Wji represents the weight from the\n","j'th Gaussian to the i'th output. 7rj is a weight attached to each basis function.\n","Such networks have been popular recently and have proven to be useful in a number of applications (e.g. (Roscheisen et al. 1992; Poggio and Edelman 1990).\n","For our current purpose, these networks have a number of advantages. Under\n","certain training regimes such as EM or \"soft clustering\" (Dempster et al. 1977;\n","Nowlan 1990) or an approximation such as K-11eans (Neal and Hinton 1993),\n","the basis functions adapt to represent local probability densities. In particular p(xaIC) :::::: E j bj(x a). If standard error gradient training is used to set the\n","weights Wij then Yi(X a ) :::::: p( Cilxa, C) Thus both (2) and (4) can be easilty computed.(Ahmad and Tresp 1993) showed that such networks can effectively learn\n","feature density information for complex visual problems. (Poggio and Edelman\n","1990) have also shown that similar networks (with a different training regime) can\n","learn to approximate the complex mappings that arise in 3D recognition.\n","\n","3.1\n","\n","OPTIMAL CORRESPONDENCE MATCHING WITH\n","OCCLUSION\n","\n","An additional advantage of G BF networks trained in this way is that it is possible\n","to obtain closed form solutions to the optimal classifier in the presence of missing\n","or noisy features. It is also possible to correctly compute the probability of feature\n","vectors containing missing dimensions. The solution consists of projecting each\n","Gaussian onto the non-missing dimensions and evaluating the resulting network.\n","Note that it is incorrect to simply substitute zero or any other single value for the\n","missing dimensions. (For lack of space we refer the reader to (Ahmad and Tresp\n","\n","\fFeature Densities Are Required for Computing Feature Correspondences\n","\n","'\"five\"\n","\n","\"four\"\n","\n","'1hree\"\n","\n","\"two\"\n","\n","\"one\"\n","\n","..tlumbs _up \" )Jointing ,.\n","\n","Figure 2: Classifiers were trained to recognize these 7 gestures. a 3D computer\n","model of the hand is used to generate images of the hand in various poses. For\n","each training example, we randomly choose a 3D orientation and depth, compute\n","the 3D positions of the fingertips and project them onto 2D. There were 5 features\n","yielding a lOD input space.\n","\n","1993) for further details.) Thus likelihood based approaches using GBF networks\n","can simultaneously optimally deal with occlusions and the correspondence problem.\n","\n","4\n","\n","EXPERIMENTAL RESULTS\n","\n","We have used the task of 3D gesture recognition to compare likelihood based methods to the network output based technique. (Figure 2 describes the task.) \"\\rVe\n","considered both rigid and non-rigid gesture recognition tasks. We used a GBF\n","network with 10 inputs, 1050 basis functions and 7 output units. For comparision\n","we also trained a standard backpropagation network (BP) with 60 hidden units on\n","the task. For this task we assume that during training all feature correspondences\n","are known and that during training no feature values are noisy or missing. For this\n","task we assume that during training all feature correspondences are known and that\n","during training no feature values are noisy or missing. Classification performance\n","with full correspondence information on an independent test set is about 92% for\n","the GBF network and 93% for the BP network. (For other results see (\\Villiams\n","et al. 1993) who have also used the rigid version of this task as a benchmark.)\n","\n","4.1\n","\n","EXPERIMENTS WITH RIGID HAND POSES\n","\n","Table 1 plots the ability of the various methods to select the correct correspondence. Random patterns were selected from the test set and all 5! = 120 possible\n","combinations were tried. MLCM denotes the percentage of times the maximum\n","likelihood method (equation (2)) selected the correct feature correspondence. GBFM and BP-M denotes how often the maximal output method chooses the correct\n","correspondence using GBF nets and BP. \"Random\" denotes the percentage if correspondences are chosen randomly. The substantially better performance of MLCM\n","suggests that, at least for this task, density information is crucial. It is also interesting to examine the errors made by MLCM. A common error is to switch the features\n","for the pinky and the adjacent finger for gestures \"one\", \"two\", \"thumbs-up\" and\n","\"pointing\". These two fingertips often project very close to one another in many\n","poses; such a mistake usually do not affect subsequent classification.\n","\n","965\n","\n","\f966\n","\n","Ahmad\n","\n","Selection Method\n","Random\n","GBF-NI\n","BP-M\n","MLCM\n","\n","Percentage Correct\n","1.2%\n","8.8%\n","10.3%\n","62.0%\n","\n","Table 1: Percentage of correspondences selected correctly.\n","\n","Classifier\n","BP-Random\n","BP-11ax\n","GBF-Max\n","GBF-vVLC\n","GBF-Known\n","\n","Classification Performance\n","28.0%\n","39.2%\n","47.3%\n","86.2%\n","91.8%\n","\n","Table 2: Classification without correspondence information.\n","\n","Table 2 shows classification performance when the correspondence is unknown.\n","GBF-WLC denotes weighted likelihood classification using GBF networks to compute the feature densities and the posterior probabilities. Performance with the\n","output based techniques are denoted with GBF-M and BP-M. BP-R denotes performance with random correspondences using the back propagation network. GBFknown plots the performance of the G BF network when all correspondences are\n","known. The results are quite encouraging in that performance is only slightly degraded with WLC even though there is substantially less information present when\n","correspondences are unknown. Although not shown, results with MLCM (i.e. not\n","doing the weighting step but just choosing the correspondence with highest probability) are about 1% less than vVLC. This supports the theory that many of the\n","errors of MLCM in Table 1 are inconsequential.\n","\n","4.1.1\n","\n","Missing Features and No Correspondences\n","\n","Figure 3 shows error as a function of the number of missing dimensions. (The\n","missing dimensions were randomly selected from the test set.) Figure 3 plots the\n","average number of classes that are assigned higher probability than the correct class.\n","The network output method and weighted likelihood classification is compared to\n","the case where all correspondences are known. In all cases the basis functions\n","were projected onto the non-missing dimensions to approximate the Bayes-optimal\n","condition. As before, the likelihood based method outperforms the output based\n","method. Surprisingly, even with 4 of the 10 dimensions missing and with correspondences unknown, \\VLC assigns highest probability to the correct class on average\n","(performance score < 1.0).\n","\n","\fFeature Densities Are Required for Computing Feature Correspondences\n","\n","Error vs missing features without correspondence\n","\n","3.5\n","3\n","2.5\n","\n","GBF-M +-WLC -eG BF-Known +--\n","\n","2\n","Error\n","\n","1.5\n","1\n","\n","0.5 rL-_-~-::r-\n","\n","o\n","\n","L -_ _ _ _ _ _\n","\n","o\n","\n","~\n","\n","________\n","\n","1\n","\n","~\n","\n","_ _ _ _ _ _ _ __ L_ _ _ _ _ _ _ _\n","\n","2\n","\n","3\n","\n","~\n","\n","______\n","\n","4\n","\n","~\n","\n","5\n","\n","No. of missing features\n","\n","Figure 3: Error with mIssmg features when no correspondence information is\n","present. The y-axis denotes the average number of classes that are assigned higher\n","probability than the correct class.\n","\n","4.2\n","\n","EXPERIMENTS WITH NON-RIGID HAND POSES\n","\n","In the previous experiments the hand configuration for each gesture remained rigid.\n","Correspondence selection with non-rigid gestures was also tried out. As before a\n","training set consisting of examples of each gesture was constructed. However, in\n","this case, for each sample, a random perturbation (within 20 degrees) was added to\n","each finger joint. The orientation of each sample was allowed to randomly vary by\n","45 degrees around the x, y, and z axes. When viewed on a screen the samples give\n","the appearance of a hand wiggling around. Surprisingly, GBF networks with 210\n","hidden units consistently selected the correct correspondences with a performance\n","of 94.9%. (The performance is actually better than the rigid case. This is because\n","in this training set all possible 3D orientations were not allowed.)\n","\n","5\n","\n","DISCUSSION\n","\n","We have shown that estimates of joint feature densities can be used to successfully\n","deal with lack of correspondence information even when some input features are\n","missing. We have dealt mainly with the rather severe case where no prior information about correspondences is available. In this particular case to get the optimal\n","correspondece, all n! possibilities must be considered. However this is usually not\n","necessary. Useful techniques exist for reducing the number of possible correspondences. For example, (Huttenlocher and Ullman 1990) have argued that three fea-\n","\n","967\n","\n","\f968\n","\n","Ahmad\n","\n","ture correspondences are enough to constrain the pose of rigid objects. In this case\n","only O(n 3 ) matches need to be tested. In addition features usually fall into incompatible sets (e.g. edge features, corner features: etc.) further reducing the nWllber\n","of potential matches. Finally: with ullage sequences one can use correspondence\n","ulformation from the previous frame to constraul the set of correspondences in the\n","current frame. \\\\llatever the situation, a likelihood based approach is a prulcipled\n","method for evaluatulg the set of available matches.\n","\n","Acknowledgements\n","1'luch of this research was conducted at Siemens Central Research in :Munich, Germany. I would like to thank Volker Tresp at Siemens for many interesting discussions\n","and Brigitte \\Virtz for providulg the hand model.\n","\n","References\n","Ahmad, S. and V. Tresp (1993). Some solutions to the missing feature problem\n","Ul vision. In S. Hanson, J. Cowan: and C. Giles (Eds.), Advances in Neural\n","Information Processing Systems 5, pp. 393 400. 110rgan Kaufmann Publishers.\n","Connell, J. and 1'1. Brady (1987). Generating and generalizing models of visual\n","objects. A1?tificial Intelligence 31: 159 183.\n","Dempster, A.: N. Laird: and D. Rubin (1977). 1tlaximwu-likelihood fromulcomplete data via the E1'1 algorithm. J. Royal Statistical Soc. Ser. B 39, 1 38.\n","Huttenlocher: D. and S. Ullman (1990). Recognizing solid objects by alignment\n","with an ullage. International Journal of Computer Vision 5(2), 195 212.\n","Neal, R. and G. HiIlton (1993). A new view of the E1tl algorithm that justifies\n","incremental and other variants. Biometrika, submitted.\n","Nowlan: S. (1990). 1/1aximwll likelihood competitive learnulg. III D. Touretzky\n","(Ed.), Advances in Neural Information Processing Systems 2: pp. 574 582.\n","San 1tlateo: CA: 1tlorgan Kaufmann Publishers.\n","Poggio, T. and S. Edelman (1990). A network that learns to recognize threediIllensional objects. Nature 343(6225), 1 3.\n","Pope: A. and D. Lowe (1993: 1tIay). Learuulg object recognition models from llllages. In Fourth International Confe1'ence on Computer Vision, Berlin. IEEE\n","Computer Society Press.\n","Roscheisen: ~I.: R. Hofmann, and V. Tresp (1992). Neural control for rolling\n","mills: Incorporating domain theories to overcome data deficiency. In 1?1. J.,\n","H. S.J.: and L. R. (Eds.), Advances in Neural Information Processing Systems\n","4. 1'Iorgan Kaufman.\n","Segen: J. (1989). 1:1odel learning and recognition of nonrigid objects. In IEEE\n","Computer Society Conference on Computer Vision and Pattern Recognition:\n","San Diego: CA.\n","\\Villiams: C. K.: R. S. Zemel: and ~I. C. 1/10zer (1993). Unsupervised learning\n","of object models. In AAAI Fall 1993 Symposium on Machine Lea?ming in\n","Computer Vision: pp. 20 24. Proceedings available as AAAI Tech Report\n","FSS-93-04.\n","\n","\f\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3mKdoSZ75ayu","executionInfo":{"status":"ok","timestamp":1638378222181,"user_tz":-60,"elapsed":264,"user":{"displayName":"Gian Carlo Milanese","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"08352423327035271918"}},"outputId":"916a4205-f171-442f-a856-ac1960a6e0f8"},"source":["print(papers.summary.iloc[0].replace('.', '.\\n'))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["We propose \"likelihood based correspondence matching\" as a general principle for selecting optimal correspondences.\n",".\n","For this task we assume that during training all feature correspondences are known and that during training no feature values are noisy or missing.\n",".\n","For this task we assume that during training all feature correspondences are known and that during training no feature values are noisy or missing.\n",".\n","Missing Features and No Correspondences Figure shows error as a function of the number of missing dimensions.\n",".\n","Feature Densities Are Required for Computing Feature Correspondences Error vs missing features without correspondence .\n","\n"]}]}]}